@article{ConvLSTM,
	archiveprefix = {arXiv},
	author = {Shi, Xingjian and Chen, Zhourong and Wang, Hao and Yeung, Dit{-}Yan and Wong, Wai{-}Kin and Woo, Wang{-}chun},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/bib/journals/corr/ShiCWYWW15},
	eprint = {1506.04214},
	journal = {CoRR},
	timestamp = {Mon, 13 Aug 2018 16:48:00 +0200},
	title = {Convolutional {LSTM} Network: {A} Machine Learning Approach for Precipitation Nowcasting},
	url = {http://arxiv.org/abs/1506.04214},
	volume = {abs/1506.04214},
	year = {2015}
}

@article{LSTM,
	acmid = {1246450},
	address = {Cambridge, MA, USA},
	author = {Hochreiter, Sepp and Schmidhuber, J{\"u}rgen},
	doi = {10.1162/neco.1997.9.8.1735},
	issn = {0899-7667},
	issue_date = {November 15, 1997},
	journal = {Neural Comput.},
	month = Nov,
	number = {8},
	numpages = {46},
	pages = {1735--1780},
	publisher = {MIT Press},
	title = {Long Short-Term Memory},
	url = {http://dx.doi.org/10.1162/neco.1997.9.8.1735},
	volume = {9},
	year = {1997}
}

@article{SegLSTM,
	archiveprefix = {arXiv},
	author = {Chen, Jianxu and Yang, Lin and Zhang, Yizhe and Alber, Mark S. and Chen, Danny Z.},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/bib/journals/corr/ChenYZAC16},
	eprint = {1609.01006},
	journal = {CoRR},
	timestamp = {Mon, 13 Aug 2018 16:46:15 +0200},
	title = {Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation},
	url = {http://arxiv.org/abs/1609.01006},
	volume = {abs/1609.01006},
	year = {2016}
}
  
@article{att,
    author = {Yu, Yannan and Xie, Yuan and Thamm, Thoralf and Gong, Enhao and Ouyang, Jiahong and Huang, Charles and Christensen, Soren and Marks, Michael P. and Lansberg, Maarten G. and Albers, Gregory W. and Zaharchuk, Greg},
    title = "{Use of Deep Learning to Predict Final Ischemic Stroke Lesions From Initial Magnetic Resonance Imaging}",
    journal = {JAMA Network Open},
    volume = {3},
    number = {3},
    pages = {e200772-e200772},
    year = {2020},
    month = {03},
    issn = {2574-3805},
    doi = {10.1001/jamanetworkopen.2020.0772},
    url = {https://doi.org/10.1001/jamanetworkopen.2020.0772},
    eprint = {https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2762679/yu\_2020\_oi\_200050\_1602778823.39892.pdf},
}


@article{BiRNN,
	author = {Schuster, M. and Paliwal, K. K.},
	doi = {10.1109/78.650093},
	issn = {1053-587X},
	journal = {IEEE Transactions on Signal Processing},
	keywords = {speech recognition; pattern classification; statistical analysis; recurrent neural nets; learning by example; speech processing; bidirectional recurrent neural networks; training; regular recurrent neural network; negative time direction; positive time direction; classification experiments; regression experiments; artificial data; phonemes; TIMIT database; conditional posterior probability; complete symbol sequences; real data; speech recognition; learning from examples; Recurrent neural networks; Artificial neural networks; Training data; Databases; Probability; Shape; Parameter estimation; Speech recognition; Control systems; Telecommunication control},
	month = {Nov},
	number = {11},
	pages = {2673--2681},
	title = {Bidirectional recurrent neural networks},
	volume = {45},
	year = {1997}
}

@article{ReviewRNN,
	archiveprefix = {arXiv},
	author = {Salehinejad, Hojjat and Baarbe, Julianne and Sankar, Sharan and Barfett, Joseph and Colak, Errol and Valaee, Shahrokh},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/bib/journals/corr/abs-1801-01078},
	eprint = {1801.01078},
	journal = {CoRR},
	timestamp = {Mon, 13 Aug 2018 16:49:11 +0200},
	title = {Recent Advances in Recurrent Neural Networks},
	url = {http://arxiv.org/abs/1801.01078},
	volume = {abs/1801.01078},
	year = {2018}
}

@inproceedings{4DLSTM,
	author = {Gao, Y. and Phillips, J. M. and Zheng, Y. and Min, R. and Fletcher, P. T. and Gerig, G.},
	booktitle = {2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)},
	doi = {10.1109/ISBI.2018.8363764},
	issn = {1945-8452},
	keywords = {biomedical MRI; computer vision; diseases; image classification; image segmentation; image sequences; learning (artificial intelligence); medical image processing; paediatrics; recurrent neural nets; image sequences; disease progression; short-term memory units; recurrent neural networks; pixel-wise segmentation; fully convolutional networks; state-of-the-art results; deep learning approaches; traditional methods; robust joint 4D segmentation; consistent joint 4D segmentation; developmental trajectories; longitudinal medical image analysis; joint 4D medical image segmentation; convolutional structured LSTM networks; longitudinal pediatric magnetic resonance imaging segmentation; temporal model; spatial model; FCNs; 4D image segmentation; sequence-to-sequence modeling; Image segmentation; Magnetic resonance imaging; Computer architecture; Biomedical imaging; Recurrent neural networks; Three-dimensional displays; Training},
	month = {April},
	number = {},
	pages = {1104--1108},
	title = {Fully convolutional structured LSTM networks for joint 4D medical image segmentation},
	volume = {},
	year = {2018}
}

@article{AllConv,
	archiveprefix = {arXiv},
	author = {Springenberg, Jost Tobias and Dosovitskiy, Alexey and Brox, Thomas and Riedmiller, Martin A.},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/bib/journals/corr/SpringenbergDBR14},
	eprint = {1412.6806},
	journal = {CoRR},
	timestamp = {Mon, 13 Aug 2018 16:45:58 +0200},
	title = {Striving for Simplicity: The All Convolutional Net},
	url = {http://arxiv.org/abs/1412.6806},
	volume = {abs/1412.6806},
	year = {2014}
}

@article{UNET,
	archiveprefix = {arXiv},
	author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/bib/journals/corr/RonnebergerFB15},
	eprint = {1505.04597},
	journal = {CoRR},
	timestamp = {Mon, 13 Aug 2018 16:46:52 +0200},
	title = {U-Net: Convolutional Networks for Biomedical Image Segmentation},
	url = {http://arxiv.org/abs/1505.04597},
	volume = {abs/1505.04597},
	year = {2015}
}

@article{ELU,
	archiveprefix = {arXiv},
	author = {Clevert, Djork{-}Arn{\'e} and Unterthiner, Thomas and Hochreiter, Sepp},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/bib/journals/corr/ClevertUH15},
	eprint = {1511.07289},
	journal = {CoRR},
	timestamp = {Mon, 13 Aug 2018 16:49:07 +0200},
	title = {Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)},
	url = {http://arxiv.org/abs/1511.07289},
	volume = {abs/1511.07289},
	year = {2015}
}

@article{CellCLSConvLSTMTM,
	archiveprefix = {arXiv},
	author = {Arbelle, Assaf and Raviv, Tammy Riklin},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/bib/journals/corr/abs-1805-11247},
	eprint = {1805.11247},
	journal = {CoRR},
	timestamp = {Mon, 13 Aug 2018 16:48:26 +0200},
	title = {Microscopy Cell Segmentation via Convolutional {LSTM} Networks},
	url = {http://arxiv.org/abs/1805.11247},
	volume = {abs/1805.11247},
	year = {2018}
}

@article{PyramidLSTM,
	archiveprefix = {arXiv},
	author = {Stollenga, Marijn F. and Byeon, Wonmin and Liwicki, Marcus and Schmidhuber, J{\"u}rgen},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/bib/journals/corr/StollengaBLS15},
	eprint = {1506.07452},
	journal = {CoRR},
	timestamp = {Mon, 13 Aug 2018 16:49:05 +0200},
	title = {Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation},
	url = {http://arxiv.org/abs/1506.07452},
	volume = {abs/1506.07452},
	year = {2015}
}

@article{TFUnet,
	author = {Akeret, Joel and Chang, Chihway and Lucchi, Aurelien and Refregier, Alexandre},
	journal = {Astronomy and Computing},
	pages = {35--39},
	publisher = {Elsevier},
	title = {Radio frequency interference mitigation using deep convolutional neural networks},
	volume = {18},
	year = {2017}
}

@article{Kidwell2729,
	abstract = {Background--- The ability to quickly and efficiently identify the ischemic penumbra in the acute stroke clinical setting is an important goal for stroke researchers and clinicians. Early and accurate identification of potentially salvageable versus irreversibly infarcted brain tissue may enable selecting the most appropriate candidates for early stroke therapies and identifying patients who may still benefit from late recanalization or neuroprotective treatment. Recent advances in magnetic resonance imaging of the ischemic penumbra have been driven by serial MRI studies characterizing the natural evolution of cerebral infarction and the brain{\rq}s response to reperfusion. Summary of Comment--- these studies have proposed various models for imaging the penumbra with MRI, including the pioneering diffusion-perfusion mismatch model and later multivariate approaches. Each model has its own unique advantages and disadvantages. Conclusions--- There now are sufficient data to support paradigm shifts in a variety of central tenets regarding MRI and the ischemic penumbra. These include the insights that diffusion-perfusion mismatch does not optimally define the penumbra; that early diffusion lesions are in part reversible and often include both irreversibly infarcted tissue and penumbra; that the visible zone of perfusion abnormality overestimates the penumbra by including regions of benign oligemia; that MRI is a very practical method for acute stroke imaging; and that therapeutic salvage of the ischemic penumbra has been demonstrated in humans using diffusion-perfusion MRI.},
	author = {Kidwell, Chelsea S. and Alger, Jeffry R. and Saver, Jeffrey L.},
	doi = {10.1161/01.STR.0000097608.38779.CC},
	eprint = {http://stroke.ahajournals.org/content/34/11/2729.full.pdf},
	issn = {0039-2499},
	journal = {Stroke},
	number = {11},
	pages = {2729--2735},
	publisher = {American Heart Association, Inc.},
	title = {Beyond Mismatch},
	url = {http://stroke.ahajournals.org/content/34/11/2729},
	volume = {34},
	year = {2003}
}

@inbook{TofLabel,
	author = {Bilgel, Murat and Roy, Snehashis and Carass, Aaron and Nyquist, {Paul A.} and Prince, {Jerry L.}},
	booktitle = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},
	doi = {10.1117/12.2006460},
	isbn = {9780819494436},
	keywords = {Automated labeling of vessels; Belief propagation; Cerebral arteries; Random forest; Statistical inference on bayesian networks},
	title = {Automated anatomical labeling of the cerebral arteries using belief propagation},
	volume = {8669},
	year = {2013}
}

@article{TofSegment,
	author = {{Hassouna M. Sabry} and {Farag A.A.} and {Hushek Stephen} and {Moriarty Thomas}},
	doi = {10.1016/j.media.2004.11.009; 10.1016/j.media.2004.11.009},
	issn = {1361-8415},
	journal = {Medical Image Analysis},
	month = jan,
	note = {doi: 10.1016/j.media.2004.11.009},
	number = {1},
	pages = {2--18},
	publisher = {Elsevier},
	risfield_0_t2 = {Medical Image Analysis},
	risfield_1_m3 = {doi: 10.1016/j.media.2004.11.009},
	title = {Cerebrovascular segmentation from TOF using stochastic models},
	volume = {10},
	year = {2005}
}

@article{Kamalian3553,
	abstract = {Background and Purpose---Thin-section noncontrast computed tomography images can be used to measure hyperdense clot length in acute ischemic stroke. Clots >=8 mm have a very low probability of intravenous tissue-type plasminogen activator recanalization and hence may benefit from a bridging intra-arterial approach. To understand the prevalence of such clots, we sought to determine the distribution and predictors of clot lengths in consecutive anterior circulation proximal artery occlusions. Methods---Of 623 consecutive patients with acute ischemic stroke, 53 met inclusion criteria: presentation \&lt;8 hours from onset; intracranial internal carotid artery-terminus or proximal-middle cerebral artery occlusion; admission thin-slice noncontrast computed tomography (<=2.5 mm); and no intravenous tissue-type plasminogen activator pretreatment. For each patient, hyperdense clot length was measured and recorded along with additional relevant imaging and clinical data. Results---Mean age was 70 years, and mean time to computed tomography was 213 minutes. Median baseline National Institutes of Health Stroke Scale was 16.5. Occlusions were located in the internal carotid artery-terminus (34\% [18 of 53]), middle cerebral artery M1 (49\% [26 of 53]) and M2 segments (17\% [9 of 53]). Hyperdense thrombus was visible in 96\%, with mean and median clot lengths (mm) of 18.5 ({\textpm}14.2) and 16.1 (7.6--25.2), respectively. Occlusion location was the strongest predictor of clot length (multivariate, P=0.02). Clot length was >=8 mm in 94\%, 73\%, and 22\% of internal carotid artery-terminus, M1, and M2 occlusions, respectively. Conclusions---The majority of anterior circulation proximal occlusions are >=8 mm long, helping to explain the low published rates of intravenous tissue-type plasminogen activator recanalization. Internal carotid artery-terminus occlusion is an excellent marker for clot length >=8 mm; vessel-imaging status alone may be sufficient. Thin-section noncontrast computed tomography seems useful for patients with middle cerebral artery occlusion because of the wide variability of clot lengths.},
	author = {Kamalian, Shervin and Morais, Livia T. and Pomerantz, Stuart R. and Aceves, Mario and Sit, Siu Po and Bose, Arani and Hirsch, Joshua A. and Lev, Michael H. and Yoo, Albert J.},
	doi = {10.1161/STROKEAHA.113.003079},
	eprint = {http://stroke.ahajournals.org/content/44/12/3553.full.pdf},
	issn = {0039-2499},
	journal = {Stroke},
	number = {12},
	pages = {3553--3556},
	publisher = {American Heart Association, Inc.},
	title = {Clot Length Distribution and Predictors in Anterior Circulation Stroke},
	url = {http://stroke.ahajournals.org/content/44/12/3553},
	volume = {44},
	year = {2013}
}

@article{CTAuto1,
	abstract = {Background and Purpose Thrombus characterization is increasingly considered important in predicting treatment success for patients with acute ischemic stroke. The lack of intensity contrast between thrombus and surrounding tissue in CT images makes manual delineation a difficult and time consuming task. Our aim was to develop an automated method for thrombus measurement on CT angiography and validate it against manual delineation.  Materials and Methods Automated thrombus segmentation was achieved using image intensity and a vascular shape prior derived from the segmentation of the contralateral artery. In 53 patients with acute ischemic stroke due to proximal intracranial arterial occlusion, automated length and volume measurements were performed. Accuracy was assessed by comparison with inter-observer variation of manual delineations using intraclass correlation coefficients and Bland--Altman analyses.  Results The automated method successfully segmented the thrombus for all 53 patients. The intraclass correlation of automated and manual length and volume measurements were 0.89 and 0.84. Bland-Altman analyses yielded a bias (limits of agreement) of ?0.4 (?8.8, 7.7) mm and 8 (?126, 141) mm3 for length and volume, respectively. This was comparable to the best interobserver agreement, with an intraclass correlation coefficients of 0.90 and 0.85 and a bias (limits of agreement) of ?0.1 (?11.2, 10.9) mm and ?17 (?216, 185) mm3.  Conclusions The method facilitates automated thrombus segmentation for accurate length and volume measurements, is relatively fast and requires minimal user input, while being insensitive to high hematocrit levels and vascular calcifications. Furthermore, it has the potential to assess thrombus characteristics of low-density thrombi.},
	author = {Santos, Emilie M. M. AND Marquering, Henk A. AND Berkhemer, Olvert A. AND van Zwam, Wim H. AND van der Lugt, Aad AND Majoie, Charles B. AND Niessen, Wiro J. AND on behalf of the MR CLEAN investigators},
	doi = {10.1371/journal.pone.0101985},
	journal = {PLOS ONE},
	month = {07},
	number = {7},
	pages = {1--8},
	publisher = {Public Library of Science},
	title = {Development and Validation of Intracranial Thrombus Segmentation on CT Angiography in Patients with Acute Ischemic Stroke},
	url = {http://dx.doi.org/10.1371%2Fjournal.pone.0101985},
	volume = {9},
	year = {2014}
}

@article{CTAuto2,
	abstract = {Computed tomography images are widely used in the diagnosis of ischemic stroke because of its faster acquisition and compatibility with most life support devices. This paper presents a new approach to automated detection of ischemic stroke using segmentation, midline shift and image feature characteristics, which separate the ischemic stroke region from healthy tissues in computed tomography images. The proposed method consists of five stages namely, pre-processing, segmentation, tracing midline of the brain, extraction of texture features and classification. The application of the proposed method for early detection of ischemic stroke is demonstrated to improve efficiency and accuracy of clinical practice. The results are quantitatively evaluated by a human expert. The average overlap metric, average precision and average recall between the results obtained using the proposed approach and the ground truth are 0.98, 0.99 and 0.98, respectively. A classification with accuracy of 98\%, 97\%, 96\% and 92\% has been obtained by SVM, k-NN, \{ANN\} and decision tree. },
	author = {Rajini, N. Hema and Bhavani, R.},
	doi = {10.1016/j.measurement.2013.01.010},
	issn = {0263-2241},
	journal = {Measurement },
	keywords = {Computed tomography (CT); Segmentation; Midline shift; Ischemic stroke },
	note = {},
	number = {6},
	pages = {1865--1874},
	title = {Computer aided detection of ischemic stroke using segmentation and texture features },
	url = {//www.sciencedirect.com/science/article/pii/S0263224113000250},
	volume = {46},
	year = {2013}
}

@article{DiffLesionSegment,
	abstract = {Motivation Ischemic stroke, triggered by an obstruction in the cerebral blood supply, leads to infarction of the affected brain tissue. An accurate and reproducible automatic segmentation is of high interest, since the lesion volume is an important end-point for clinical trials. However, various factors, such as the high variance in lesion shape, location and appearance, render it a difficult task.   Methods In this article, nine classification methods (e.g. Generalized Linear Models, Random Decision Forests and Convolutional Neural Networks) are evaluated and compared with each other using 37 multiparametric MRI datasets of ischemic stroke patients in the sub-acute phase in terms of their accuracy and reliability for ischemic stroke lesion segmentation. Within this context, a multi-spectral classification approach is compared against mono-spectral classification performance using only FLAIR MRI datasets and two sets of expert segmentations are used for inter-observer agreement evaluation.   Results and Conclusion The results of this study reveal that high-level machine learning methods lead to significantly better segmentation results compared to the rather simple classification methods, pointing towards a difficult non-linear problem. The overall best segmentation results were achieved by a Random Decision Forest and a Convolutional Neural Networks classification approach, even outperforming all previously published results. However, none of the methods tested in this work are capable of achieving results in the range of the human observer agreement and the automatic ischemic stroke lesion segmentation remains a complicated problem that needs to be explored in more detail to improve the segmentation results.},
	author = {Maier, Oskar AND Schr{\"o}der, Christoph AND Forkert, Nils Daniel AND Martinetz, Thomas AND Handels, Heinz},
	doi = {10.1371/journal.pone.0145118},
	journal = {PLOS ONE},
	month = {12},
	number = {12},
	pages = {1--16},
	publisher = {Public Library of Science},
	title = {Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study},
	url = {http://dx.doi.org/10.1371%2Fjournal.pone.0145118},
	volume = {10},
	year = {2015}
}

@article{MONSTR,
	author = {Roy, Snehashis and Butman, John A. and Pham, Dzung L.},
	journal = {NeuroImage},
	pages = {132--147},
	title = {Robust skull stripping using multiple MR image contrasts insensitive to pathology},
	volume = {146},
	year = {2017}
}

@book{HandBookBSS,
	author = {Comon, Pierre and Jutten, Christian},
	edition = {1st},
	isbn = {0123747260, 9780123747266},
	publisher = {Academic Press},
	title = {Handbook of Blind Source Separation: Independent Component Analysis and Applications},
	year = {2010}
}

@article{NMFOrig,
	author = {{Lee Daniel D.} and {Seung H. Sebastian}},
	doi = {10.1038/44565},
	issn = {0028-0836},
	journal = {Nature},
	month = oct,
	note = {10.1038/44565},
	number = {6755},
	pages = {788--791},
	risfield_0_m3 = {10.1038/44565},
	title = {Learning the parts of objects by non-negative matrix factorization},
	volume = {401},
	year = {1999}
}

@article{SparseNMF,
	acmid = {1044709},
	author = {Hoyer, Patrik O.},
	issn = {1532-4435},
	issue_date = {12/1/2004},
	journal = {J. Mach. Learn. Res.},
	month = dec,
	numpages = {13},
	pages = {1457--1469},
	publisher = {JMLR.org},
	title = {Non-negative Matrix Factorization with Sparseness Constraints},
	url = {http://dl.acm.org/citation.cfm?id=1005332.1044709},
	volume = {5},
	year = {2004}
}

@article{VesselSegReview,
	author = {Lesage, David and Angelini, Elsa D. and Bloch, Isabelle and Funka-Lea, Gareth},
	doi = {10.1016/j.media.2009.07.011},
	issn = {1361-8415},
	journal = {Medical Image Analysis},
	keywords = {Review; Vascular segmentation; Vascular models; Vessel-dedicated features; Segmentation algorithms; 3D angiography; Computed tomography angiography (CTA); Magnetic resonance angiography (MRA)},
	note = {Includes Special Section on Computational Biomechanics for Medicine},
	number = {6},
	pages = {819--845},
	title = {A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes},
	url = {http://www.sciencedirect.com/science/article/pii/S136184150900067X},
	volume = {13},
	year = {2009}
}

@article{SatoHessian,
	author = {Sato, Yoshinobu and Westin, Carl-Fredrik and Bhalerao, Abhir and Nakajima, Shin and Shiraga, Nobuyuki and Tamura, Shinichi and Kikinis, Ron},
	journal = {IEEE Trans. Vis. Comput. Graph.},
	pages = {160--180},
	title = {Tissue Classification Based on 3D Local Intensity Structures for Volume Rendering},
	volume = {6},
	year = {2000}
}

@article{FrangiHessian,
	author = {Frangi, Alejandro F. and Niessen, Wiro J. and Hoogeveen, Romhild M. and van Walsum, Theo and Viergever, Max A.},
	journal = {IEEE transactions on medical imaging},
	pages = {946--56},
	title = {Model-Based Quantitation of 3D Magnetic Resonance Angiographic Images},
	volume = {18 10},
	year = {1999}
}

@article{Watershed,
	author = {Meyer, Fernand},
	doi = {10.1016/0165-1684(94)90060-4},
	issn = {0165-1684},
	journal = {Signal Processing},
	keywords = {Topographic distance; Watershed line; Segmentation; Shortest path algorithms; Mathematical morphology},
	note = {Mathematical Morphology and its Applications to Signal Processing},
	number = {1},
	pages = {113--125},
	title = {Topographic distance and watershed lines},
	url = {http://www.sciencedirect.com/science/article/pii/0165168494900604},
	volume = {38},
	year = {1994}
}

@inbook{ThrombusAorta,
	abstract = {In this paper, a method for segmenting the epithelial (outer) layer of aortic aneurysms in CT-angiography acquisitions is presented. The method starts by computing the lumen centerline between two user-defined seed points which serve as bounds (proximal and distal) to the extent of the lesion. This centerline determines the Multi-Planar Reconstruction (MPR) planes which display the vessel cross-sections. Then, the user manually segments the epithelial layer on the first proximal MPR plane to form a contour. From this point, a loop is executed: First, the contour is used to set up a restricted search region in the adjacent distal MPR plane along the centerline. The voxels of the adjacent MPR plane within this search region and the voxels on which the contour itself sits are used to create a directed graph. Then, the mincut of the graph is calculated, creating an optimal segmentation of the Aortic Aneurysm border on the adjacent MPR plane. This segmented contour serves as a starting point for the loop, which is then executed again.},
	address = {Berlin, Heidelberg},
	author = {Egger, J. and O'Donnell, Thomas and Hopfgartner, C. and Freisleben, B.},
	booktitle = {4th European Conference of the International Federation for Medical and Biological Engineering: ECIFMBE 2008 23--27 November 2008 Antwerp, Belgium},
	doi = {10.1007/978-3-540-89208-3},
	editor = {{Vander Sloten}, Jos and Verdonck, Pascal and Nyssen, Marc and Haueisen, Jens},
	isbn = {978-3-540-89208-3},
	pages = {584--587},
	publisher = {Springer Berlin Heidelberg},
	title = {Graph-based Tracking Method for Aortic Thrombus Segmentation},
	year = {2009}
}

@article{ThrombusAbdomen,
	author = {Olabarriaga, S. D. and Rouet, J. M. and Fradkin, M. and Breeuwer, M. and Niessen, W. J.},
	doi = {10.1109/TMI.2004.843260},
	issn = {0278-0062},
	journal = {IEEE Transactions on Medical Imaging},
	keywords = {biomechanics; computerised tomography; deformation; diagnostic radiography; diseases; image classification; image segmentation; medical image processing; optimisation; abdominal aortic aneurysms; computed tomography angiography; deformable model-based segmentation; deformation; k-nearest neighbors; nonparametric pattern classification; nonparametric statistical grey level appearance modeling; parameter optimization; thrombus; Abdomen; Aneurysm; Angiography; Biomedical imaging; Computed tomography; Deformable models; Image reconstruction; Image segmentation; Pattern classification; Volume measurement; Abdominal aortic aneurysm; deformable models; image segmentation; statistical grey level modeling; thrombus segmentation; Algorithms; Angiography; Aortic Aneurysm, Abdominal; Artificial Intelligence; Cluster Analysis; Computer Graphics; Humans; Imaging, Three-Dimensional; Models, Cardiovascular; Models, Statistical; Pattern Recognition, Automated; Radiographic Image Enhancement; Radiographic Image Interpretation, Computer-Assisted; Reproducibility of Results; Sensitivity and Specificity; Thrombosis; Tomography, X-Ray Computed},
	month = {April},
	number = {4},
	pages = {477--485},
	title = {Segmentation of thrombus in abdominal aortic aneurysms from CTA with nonparametric statistical grey level appearance modeling},
	volume = {24},
	year = {2005}
}

@article{CTThrombusSemiThreshold,
	abstract = {Introduction: The hyperdense sign is a marker of thrombus on non-contrast computed tomography (NCCT). Currently, pre-specified Hounsfield unit (HU) threshold are used arbitrarily for clot segmentation. We aimed to test if HU thresholds best discriminating clot on thin slice NCCT are patient specific and dependent on factors such as age, hematocrit, and NCCT slice thickness.Methods: Data are from the ESCAPE randomized controlled trial. Only patients with thin slice baseline NCCT (\&lt;2.5mm) and M1-MCA occlusion on baseline CT-Angiography (CTA) were included. CTA was co-registered to NCCT using in-house software. Proximal and distal clot interface was identified on CTA and super-imposed onto the NCCT. Three Regions of Interest (ROIs) (36- 100 voxels) were placed on the NCCT in the 1) clot as defined on co-registered CTA, 2) contralateral brain tissue, and 3) contralateral patent M1 MCA artery. Optimal patient specific HU thresholds differentiating ``clot'' from ``normal vessel'' and ``brain tissue'' voxels were calculated using receiver operating characteristic (ROC) analysis.Results: 70 patients were included. ROC analysis showed that the optimal HU threshold discriminating clot on NCCT varies considerably between patients (Figure 1A). Hematocrit was associated with contralateral artery HU (p\&lt;0.001); no significant correlation was found with age (p=0.156) and slice thickness (p=0.473). Predictive ability of contralateral artery and brain tissue HU in determining optimal HU threshold discriminating clot was similar (p\&lt;0.001, Figure 1B). Model comparison using Bayesian Information Criterion suggests that we can reasonably use contralateral brain tissue HU or contralateral artery HU to derive patient-specific optimal HU thresholds that best discriminates clot.Conclusion: HU thresholds on NCCT that best discriminates clot are patient specific. Signal from brain tissue or patent artery silhouette can be used to derive these patient specific thresholds.Author Disclosures: E. Qazi: None. A.T. Wilson: None. C. McDougall: None. M. Boesen: None. F.S. Al-Ajlan: None. P. Pordeli: None. C. Batchelor: None. K. Khan: None. T.T. Sajobi: None. T. Lee: None. M.D. Hill: None. A.M. Demchuk: None. M. Goyal: Research Grant; Significant; Funding from Covidien for design and conduct of SWIFT PRIME trial. Part funding of ESCAPE trial from Covidien provided to Univ of Calgary. Speakers{\rq} Bureau; Significant; For teaching engagements from Covidien and Stryker. C.D. d{\rq}Esterre: None. B.K. Menon: None. N.D. Forkert: None.},
	author = {Qazi, Emmad and Wilson, Alexis T and McDougall, Connor and Boesen, Mari and Al-Ajlan, Fahad S and Pordeli, Pooneh and Batchelor, Connor and Khan, Khayam and Sajobi, Tolulope T and Lee, Ting-Yim and Hill, Michael D and Demchuk, Andrew M and Goyal, Mayank and d{\rq}Esterre, Christopher D and Menon, Bijoy K and Forkert, Nils D},
	eprint = {http://stroke.ahajournals.org/content},
	issn = {0039-2499},
	journal = {Stroke},
	number = {Suppl 1},
	pages = {ATP45--ATP45},
	publisher = {American Heart Association, Inc.},
	title = {Abstract TP45: One Threshold Does not Fit All: Hounsfield Unit Thresholds to Segment Clot on NCCT are Patient Specific},
	url = {http://stroke.ahajournals.org/content/47/Suppl_1/ATP45},
	volume = {47},
	year = {2016}
}

@article{CTThrombusSemiSeed,
	abstract = {Background and Purpose Thrombus characterization is increasingly considered important in predicting treatment success for patients with acute ischemic stroke. The lack of intensity contrast between thrombus and surrounding tissue in CT images makes manual delineation a difficult and time consuming task. Our aim was to develop an automated method for thrombus measurement on CT angiography and validate it against manual delineation.  Materials and Methods Automated thrombus segmentation was achieved using image intensity and a vascular shape prior derived from the segmentation of the contralateral artery. In 53 patients with acute ischemic stroke due to proximal intracranial arterial occlusion, automated length and volume measurements were performed. Accuracy was assessed by comparison with inter-observer variation of manual delineations using intraclass correlation coefficients and Bland--Altman analyses.  Results The automated method successfully segmented the thrombus for all 53 patients. The intraclass correlation of automated and manual length and volume measurements were 0.89 and 0.84. Bland-Altman analyses yielded a bias (limits of agreement) of −0.4 (−8.8, 7.7) mm and 8 (−126, 141) mm3 for length and volume, respectively. This was comparable to the best interobserver agreement, with an intraclass correlation coefficients of 0.90 and 0.85 and a bias (limits of agreement) of −0.1 (−11.2, 10.9) mm and −17 (−216, 185) mm3.  Conclusions The method facilitates automated thrombus segmentation for accurate length and volume measurements, is relatively fast and requires minimal user input, while being insensitive to high hematocrit levels and vascular calcifications. Furthermore, it has the potential to assess thrombus characteristics of low-density thrombi.},
	author = {Santos, Emilie M. M. AND Marquering, Henk A. AND Berkhemer, Olvert A. AND van Zwam, Wim H. AND van der Lugt, Aad AND Majoie, Charles B. AND Niessen, Wiro J. AND on behalf of the MR CLEAN investigators},
	doi = {10.1371/journal.pone.0101985},
	journal = {PLOS ONE},
	month = {07},
	number = {7},
	pages = {1--8},
	publisher = {Public Library of Science},
	title = {Development and Validation of Intracranial Thrombus Segmentation on CT Angiography in Patients with Acute Ischemic Stroke},
	url = {https://doi.org/10.1371/journal.pone.0101985},
	volume = {9},
	year = {2014}
}

@article{CTThrombusSemiSeed1,
	abstract = {Background and Purpose--- We sought to evaluate how accurately length and volume of thrombotic clots occluding cerebral arteries of patients with acute ischemic stroke can be assessed from nonenhanced CT (NECT) scans reconstructed with different slice widths. Methods--- NECT image data of 58 patients with acute ischemic stroke with vascular occlusion proven by CT angiography were reconstructed with slice widths of 1.25 mm, 2.5 mm, 3.75 mm, and 5 mm. Thrombus lengths and volumes were quantified based on these NECT images by detecting and segmenting intra-arterial hyperdensities. The results were compared with reference values of thrombus length and volume obtained from CT angiography images using Bland-Altman analysis and predefined levels or tolerance to find NECT slice thicknesses that allow for sufficiently accurate thrombus quantification. Results--- Thrombus length can be measured with high accuracy using the hyperdense middle cerebral artery sign detected in NECT images with slice thicknesses of 1.25 mm and 2.5 mm. We found mean deviations from the reference values and limits of agreement of -0.1 mm{\textpm}0.6 mm with slice widths of 1.25 mm and 0.1 mm{\textpm}0.7 mm for slice widths of 2.5 mm. Thrombus length measurements in NECT images with higher slice width and all evaluated thrombus volume measurements exhibited severe dependence on the level and did not match the accuracy criteria. Conclusion--- The length of the hyperdense middle cerebral artery sign as detected on thin-slice NECT reconstructions in patients with acute ischemic stroke can be used to quantify thrombotic burden accurately. Thus, it might qualify as a new diagnostic parameter in acute stroke management that indicates and quantifies the extent of vascular obliteration.},
	author = {Riedel, Christian H. and Jensen, Ulf and Rohr, Axel and Tietke, Marc and Alfke, Karsten and Ulmer, Stephan and Jansen, Olav},
	doi = {10.1161/STROKEAHA.110.580662},
	eprint = {http://stroke.ahajournals.org/content/41/8/1659.full.pdf},
	issn = {0039-2499},
	journal = {Stroke},
	number = {8},
	pages = {1659--1664},
	publisher = {American Heart Association, Inc.},
	title = {Assessment of Thrombus in Acute Middle Cerebral Artery Occlusion Using Thin-Slice Nonenhanced Computed Tomography Reconstructions},
	url = {http://stroke.ahajournals.org/content/41/8/1659},
	volume = {41},
	year = {2010}
}

@article{DWIConvolutionalNN,
	author = {Chen, Liang and Bentley, Paul and Rueckert, Daniel},
	doi = {10.1016/j.nicl.2017.06.016},
	issn = {2213-1582},
	journal = {NeuroImage: Clinical},
	keywords = {Acute ischemic lesion segmentation; DWI; Deep learning; Convolutional neural networks},
	number = {Supplement C},
	pages = {633--643},
	title = {Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks},
	url = {http://www.sciencedirect.com/science/article/pii/S221315821730147X},
	volume = {15},
	year = {2017}
}

@article{DWIFuzzy,
	author = {Tsai, Jang-Zern and Peng, Syu-Jyun and Chen, Yu-Wei and Wang, Kuo-Wei and Wu, Hsiao-Kuang and Lin, Yun-Yu and Lee, Ying-Ying and Chen, Chi-Jen and Lin, Huey-Juan and Smith, Eric and Yeh, Poh-Shiow and Hsin, Yue-Loong},
	booktitle = {BioMed research international},
	month = {03},
	pages = {963032},
	title = {Automatic Detection and Quantification of Acute Cerebral Infarct by Fuzzy Clustering and Histographic Characterization on Diffusion Weighted MR Imaging and Apparent Diffusion Coefficient Map},
	volume = {2014},
	year = {2014}
}

@article{DWIFuzzy2,
	author = {Saad, N. Mohd and Noor, N. S. M. and {A.R. Abdullah} and Muda, Sobri and Muda, A. F. and Rahman, N. N. S. Abdul},
	journal = {IMECS 2017},
	month = mar,
	title = {Automated Stroke Lesion Detection and Diagnosis System},
	year = {2017}
}

@article{DWINNGaussianMixture,
	author = {KN, Bhanu Prakash and Gupta, Varsha and Bilello, Michel and Beauchamp, Norman and Nowinski, Wieslaw},
	booktitle = {Academic radiology},
	month = {01},
	pages = {1474--84},
	title = {Identification, Segmentation, and Image Property Study of Acute Infarcts in Diffusion-Weighted Images by Using a Probabilistic Neural Network and Adaptive Gaussian Mixture Model},
	volume = {13},
	year = {2007}
}

@article{Collateral,
	abstract = {Background--- The collateral circulation plays a pivotal role in the pathophysiology of cerebral ischemia. Current knowledge of the collateral circulation remains sparse, largely because of prior limitations in methods for evaluation of these diminutive routes of cerebral blood flow. Summary of Review--- Anatomic descriptions of the collateral circulation often focus on more proximal anastomoses at the circle of Willis, neglecting secondary collateral pathways provided by leptomeningeal vessels. Pathophysiological recruitment of collateral vessels likely depends on the temporal course of numerous compensatory hemodynamic, metabolic, and neural mechanisms. Subsequent endurance of these protective vascular pathways may determine the severity of ischemic injury. Characterization of the collateral circulation with advanced neuroimaging modalities that provide angiographic information and perfusion data may elucidate critical determinants of collateral blood flow. Such information on the status of the collateral circulation may be used to guide therapeutic interventions. Prognostication and risk stratification may also be improved by routine evaluation of collateral blood flow. Conclusions--- Contemporary understanding of the collateral circulation may be greatly enhanced through further refinement of neuroimaging modalities that correlate angiographic findings with perfusion status, providing the basis for future therapeutic and prognostic applications.},
	author = {Liebeskind, David S.},
	doi = {10.1161/01.STR.0000086465.41263.06},
	eprint = {http://stroke.ahajournals.org/content/34/9/2279.full.pdf},
	issn = {0039-2499},
	journal = {Stroke},
	number = {9},
	pages = {2279--2284},
	publisher = {American Heart Association, Inc.},
	title = {Collateral Circulation},
	url = {http://stroke.ahajournals.org/content/34/9/2279},
	volume = {34},
	year = {2003}
}

@article{NeuronIschemia,
	author = {Lee, Juhye M and Grabb, M C and Zipfel, Gregory Joseph and Choi, Dennis W .},
	journal = {The Journal of clinical investigation},
	pages = {723--31},
	title = {Brain tissue responses to ischemia.},
	volume = {106 6},
	year = {2000}
}

@article{NeuronDeathMonkey,
	abstract = {As shown previously, the electrical function of the brain is critically dependent on cerebral blood flow in the sense that reduction beyond an ischemic threshold of approximately 15 ml/100 gm per minute (approximately 35\% of control) in the baboon leads to complete failure of the somatosensory evoked response. This study tests the hypothesis that electrical failure in ischemia may be directly associated with a massive release of intracellular K+ or with a critical degree of extracellular acidosis. By microelectrode techniques, measurements of blood flow, extracellular activity of K+ and H+ as well as evoked potential were made in the baboon neocortex. Reductions in blood flow were obtained by occlusion of the middle cerebral artery and depression beyond the ischemic threshold of electrical function achieved by a reduction of systemic blood pressure which, in the ischemic zones, changed local cerebral blood flow proportionally. Abolition of evoked response could not be explained by depolarization by release of intracellular K+, nor was it critically dependent on cortical pH. However, the massive release of intracellular K+ was by itself critically dependent on cortical blood flow and occurred at 18 greater than 6 greater than 2 ml/100 gm per minute (median with 5\% confidence limits). Thus a dual threshold in ischemia for neuronal function is described, the threshold for release of K+ being clearly lower than the threshold for complete electrical failure. Further, the findings support the concept of an ischemic penumbra during which the neurons remain structurally intact but functionally inactive. That neurons can survive for some time in this state of lethargy is evidenced by the observations that an increase in rCBF, if sufficient, can restore evoked potential and normalize extracellular K+ activity as well as pH.},
	author = {Astrup, J and Symon, L and Branston, N M and Lassen, N A},
	doi = {10.1161/01.STR.8.1.51},
	eprint = {http://stroke.ahajournals.org/content/8/1/51.full.pdf},
	issn = {0039-2499},
	journal = {Stroke},
	number = {1},
	pages = {51--57},
	publisher = {American Heart Association, Inc.},
	title = {Cortical evoked potential and extracellular K+ and H+ at critical levels of brain ischemia.},
	url = {http://stroke.ahajournals.org/content/8/1/51},
	volume = {8},
	year = {1977}
}

@article{NeuronDeathCurrent1,
	author = {{Hwang Jee-Yeon} and {Gertner Michael} and {Pontarelli Fabrizio} and {Court-Vazquez Brenda} and {Bennett Michael Vander Laan} and {Ofengeim Dimitry} and {Zukin Ruth Suzanne}},
	doi = {10.1038/cdd.2016.140; 10.1038/cdd.2016.140},
	journal = {Cell Death And Differentiation},
	month = dec,
	pages = {317},
	publisher = {The Author(s)},
	risfield_0_m3 = {Original Paper},
	title = {Global ischemia induces lysosomal-mediated degradation of mTOR and activation of autophagy in hippocampal neurons destined to die},
	url = {https://www.nature.com/articles/cdd2016140\#supplementary-information},
	volume = {24},
	year = {2016}
}

@article{NeuronDeathCurrent2,
	author = {{Jeon Jaepyo} and {Sun Guanghua} and {Tian Jinbin} and {Ting Sung-Ming} and {Aronowski Jaroslaw} and {Zhu Michael X.}},
	doi = {10.1016/j.bpj.2016.11.2207; 10.1016/j.bpj.2016.11.2207},
	issn = {0006-3495},
	journal = {Biophysical Journal},
	month = nov,
	note = {doi: 10.1016/j.bpj.2016.11.2207},
	number = {3},
	pages = {408a},
	publisher = {Elsevier},
	risfield_0_t2 = {Biophysical Journal},
	risfield_1_m3 = {doi: 10.1016/j.bpj.2016.11.2207},
	title = {The Role of TRPC Channels in Ischemic Neuronal Cell Death},
	volume = {112},
	year = {2017}
}

@article{NeuronDeathCurrent3,
	abstract = { Stroke is the world's second leading cause of mortality, with a high incidence of morbidity. Numerous neuronal membrane receptors are activated by endogenous ligands and may contribute to infarct development. Notch is a well-characterized membrane receptor involved in cell differentiation and proliferation, and now shown to play a pivotal role in cell death during ischemic stroke. Blockade of Notch signaling by inhibition of \ensuremath{\gamma}-secretase, an enzyme that generates the active form of Notch, is neuroprotective following stroke. We have also identified that Pin1, a peptidyl-prolyl isomerase that regulates p53 transactivation under stress, promotes the pathogenesis of ischemic stroke via Notch signaling. Moreover, Notch can also mediate cell death through a p53-dependent pathway, resulting in apoptosis of neural progenitor cells. The current study has investigated the interplay between Notch and p53 under ischemic stroke conditions. Using pharmacological inhibitors, we have demonstrated that a Notch intracellular domain (NICD)/p53 interaction is involved in transcriptional regulation of genes downstream of p53 and NICD to modify stroke severity. Furthermore, the NICD/p53 interaction confers stability to p53 by rescuing it from ubiquitination. Together, these results indicate that Notch contributes to the pathogenesis of ischemic stroke by promoting p53 stability and signaling. },
	author = {Balaganapathy, Priyanka and Baik, Sang-Ha and Mallilankaraman, Karthik and Sobey, Christopher G and Jo, Dong-Gyu and Arumugam, Thiruma V},
	doi = {10.1177/0271678X17715956},
	eprint = {https://doi.org/10.1177/0271678X17715956 },
	journal = {Journal of Cerebral Blood Flow \& Metabolism},
	note = {PMID: 28617078},
	number = {0},
	pages = {0271678X17715956},
	title = {Interplay between Notch and p53 promotes neuronal cell death in ischemic stroke},
	url = { 
        https://doi.org/10.1177/0271678X17715956
    
},
	volume = {0},
	year = {2017}
}

@inbook{ProtonDiffusion,
	address = {Berlin, Heidelberg},
	author = {Heiland, Sabine},
	booktitle = {Magnetic Resonance Imaging in Ischemic Stroke},
	doi = {10.1007/3-540-27738-2},
	editor = {von Kummer, R{\"U}diger and Back, Tobias},
	isbn = {978-3-540-27738-5},
	pages = {103--116},
	publisher = {Springer Berlin Heidelberg},
	title = {Disturbed Brain Perfusion},
	year = {2006}
}

@article{NIHSS,
	abstract = {We designed a 15-item neurologic examination stroke scale for use in acute stroke therapy trials. In a study of 24 stroke patients, interrater reliability for the scale was found to be high (mean kappa = 0.69), and test-retest reliability was also high (mean kappa = 0.66-0.77). Test-retest reliability did not differ significantly among a neurologist, a neurology house officer, a neurology nurse, or an emergency department nurse. The stroke scale validity was assessed by comparing the scale scores obtained prospectively on 65 acute stroke patients to the patients{\rq} infarction size as measured by computed tomography scan at 1 week and to the patients{\rq} clinical outcome as determined at 3 months. These correlations (scale-lesion size r = 0.68, scale-outcome r = 0.79) suggested acceptable examination and scale validity. Of the 15 test items, the most interrater reliable item (pupillary response) had low validity. Less reliable items such as upper or lower extremity motor function were more valid. We discuss methods for improving the reliability and validity of brief examination scales to be used in stroke therapy trials.},
	author = {Brott, T and Adams, H P and Olinger, C P and Marler, J R and Barsan, W G and Biller, J and Spilker, J and Holleran, R and Eberle, R and Hertzberg, V},
	doi = {10.1161/01.STR.20.7.864},
	eprint = {http://stroke.ahajournals.org/content/20/7/864.full.pdf},
	issn = {0039-2499},
	journal = {Stroke},
	number = {7},
	pages = {864--870},
	publisher = {American Heart Association, Inc.},
	title = {Measurements of acute cerebral infarction: a clinical examination scale.},
	url = {http://stroke.ahajournals.org/content/20/7/864},
	volume = {20},
	year = {1989}
}

@article{NIHSS16,
	author = {{P Adams}, H and {H Davis}, P and Leira, Enrique and {C Chang}, K and {H Bendixen}, B and {R Clarke}, W and Woolson, Robert and {D Hansen}, M},
	booktitle = {Neurology},
	month = {07},
	pages = {126--31},
	title = {Baseline NIH Stroke Scale score strongly predicts outcome after stroke: a report of the Trial of ORG 10172 in Acute Stroke Treatment (TOAST)},
	volume = {53},
	year = {1999}
}

@article{LesionImaging,
	author = {von Kummer, R{\"u}diger and Dzialowski, Imanuel},
	booktitle = {Neuroradiology},
	month = {05},
	pages = {1--9},
	title = {Imaging of cerebral ischemic edema and neuronal death},
	volume = {59},
	year = {2017}
}

@article{CTThrombusDetection,
	abstract = {The prediction of successful recanalization following thrombolytic or endovascular treatment may be helpful to determine the strategy of recanalization treatment in acute stroke. Thrombus can be detected using noncontrast computed tomography (CT) as a hyperdense artery sign or blooming artifact on a T2(*)-weighted gradient-recalled image. The detection of thrombus using CT depends on slice thickness. Thrombus burden can be determined in terms of the length, volume, and clot burden score. The thrombus size can be quantitatively measured on thin-section CT or CT angiography/magnetic resonance angiography. The determination of thrombus size may be predictive of successful recanalization/non-recanalization after intravenous thrombolysis and endovascular treatment. However, cut-offs of thrombus size for predicting recanalization/non-recanalization are different among studies, due to different methods of measurements. Thus, a standardized method to measure the thrombus is necessary for thrombus imaging to be useful and reliable in clinical practice. Software-based measurements may provide a reliable and accurate assessment. The measurement should be easy and rapid to be more widely used in practice, which could be achieved by improvement of the user interface. In addition to prediction of recanalization, sequential measurements of thrombus volume before and after the treatment may also be useful to determine the efficacy of new thrombolytic drugs. This manuscript reviews the diagnosis of thrombus, prediction of recanalization using thrombus imaging, and practical considerations for the measurement of thrombus burden and density on CT.},
	author = {{Heo Ji Hoe} and {Kim Kyeonsub} and {Yoo Joonsang} and {Kim Young Dae} and {Nam Hyo Suk} and {Kim Eung Yeop}},
	doi = {10.5853/jos.2016.01522},
	issn = {2287-6391 2287-6405},
	journal = {Journal of Stroke},
	month = jan,
	number = {1},
	pages = {40--49},
	publisher = {Korean Stroke Society},
	risfield_0_db = {PMC},
	risfield_1_u1 = {jos-2016-01522[PII]},
	risfield_2_an = {PMC5307933},
	risfield_3_u1 = {28178411[pmid]},
	title = {Computed Tomography-Based Thrombus Imaging for the Prediction of Recanalization after Reperfusion Therapy in Stroke},
	url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5307933/},
	volume = {19},
	year = {2017}
}

@article{SpinDynamics,
	author = {Levitt, Malcolm H.},
	edition = {2},
	howpublished = {book},
	isbn = {978-0-470-51117-6},
	publisher = {Wiley},
	title = {Spin Dynamics: Basics of Nuclear Magnetic Resonance},
	year = {2008}
}

@article{MRIPhaseEncoding,
	abstract = {A new method of two- or three-dimensional spin density imaging by nuclear magnetic resonance (NMR) is proposed, which exploits the properties of spin echoes in time-dependent magnetic field gradients. An analysis shows that simultaneous observation and differentiation of signals, arising from all spins distributed in a plane or set of planes within the specimen, is possible. The method is thus capable of producing visual pictures faster than previously described planar imaging methods.},
	author = {Mansfield, P},
	journal = {Journal of Physics C: Solid State Physics},
	number = {3},
	pages = {L55},
	title = {Multi-planar image formation using NMR spin echoes},
	url = {http://stacks.iop.org/0022-3719/10/i=3/a=004},
	volume = {10},
	year = {1977}
}

@article{MRIFrequencyEncoding,
	author = {{LAUTERBUR P. C.}},
	doi = {10.1038/242190a0; 10.1038/242190a0},
	journal = {Nature},
	month = mar,
	pages = {190},
	publisher = {Nature Publishing Group},
	title = {Image Formation by Induced Local Interactions: Examples Employing Nuclear Magnetic Resonance},
	volume = {242},
	year = {1973}
}

@article{DWIOverview,
	author = {{Le Bihan}, Denis},
	doi = {10.1002/emmm.201404055},
	eprint = {http://embomolmed.embopress.org/content/6/5/569.full.pdf},
	issn = {1757-4676},
	journal = {EMBO Molecular Medicine},
	number = {5},
	pages = {569--573},
	publisher = {EMBO Press},
	title = {Diffusion MRI: what water tells us about the brain},
	url = {http://embomolmed.embopress.org/content/6/5/569},
	volume = {6},
	year = {2014}
}

@article{DWIReview,
	author = {Kiselev, Valerij},
	month = {03},
	pages = {},
	title = {Fundamentals of diffusion MRI physics},
	volume = {30},
	year = {2017}
}

@article{DWIArtifacts,
	author = {{Le Bihan}, Denis and Poupon, Cyril and Amadon, Alexis and Lethimonnier, Franck},
	doi = {10.1002/jmri.20683},
	issn = {1522-2586},
	journal = {Journal of Magnetic Resonance Imaging},
	keywords = {diffusion; artifacts; MRI physics; eddy currents; EPI},
	number = {3},
	pages = {478--488},
	publisher = {Wiley Subscription Services, Inc., A Wiley Company},
	title = {Artifacts and pitfalls in diffusion MRI},
	url = {http://dx.doi.org/10.1002/jmri.20683},
	volume = {24},
	year = {2006}
}

@article{DWIPitfalls,
	author = {Jones, Derek K. and Cercignani, Mara},
	doi = {10.1002/nbm.1543},
	issn = {1099-1492},
	journal = {NMR in Biomedicine},
	keywords = {diffusion tensor MRI; registration; distortion; spatial normalization; analysis; statistics; artefacts; voxel-based analysis},
	number = {7},
	pages = {803--820},
	publisher = {John Wiley \& Sons, Ltd.},
	title = {Twenty-five pitfalls in the analysis of diffusion MRI data},
	url = {http://dx.doi.org/10.1002/nbm.1543},
	volume = {23},
	year = {2010}
}

@article{FLAIRCollateral,
	abstract = {SUMMARY: Vascular hyperintensities have been noted on FLAIR sequences obtained in the setting of acute stroke and intracranial steno-occlusive disease. The presence of FVHs likely represents disordered blood flow, often from collaterals distal to arterial occlusion or stenosis. As opposed to other vessel signs seen in arterial insufficiency, FVH is unique in that it does not represent thrombus, but rather sluggish or disordered blood flow through vessels. This review will discuss the diagnostic and prognostic value of FVH and its impact on clinical decision-making. DWIdiffusion-weighted imagingFLAIRfluid-attenuated inversion recoveryFVHFLAIR vascular hyperintensityGRE-BAgradient-echo susceptibility vessel blooming artifactsHMCAShyperdense MCA sign seen on CTHVShyperintense vessel signMCAmiddle cerebral arteryMRAMR angiographyMRIMR imagingNIHSSNational Institutes of Health Stroke ScalePWIperfusion-weighted imagingrtPArecombinant tissue plasminogen activatorTOFtime of flightTIAtransient ischemic attacks},
	author = {Azizyan, A. and Sanossian, N. and Mogensen, M.A. and Liebeskind, D.S.},
	doi = {10.3174/ajnr.A2265},
	eprint = {http://www.ajnr.org/content/32/10/1771.full.pdf},
	issn = {0195-6108},
	journal = {American Journal of Neuroradiology},
	number = {10},
	pages = {1771--1775},
	publisher = {American Journal of Neuroradiology},
	title = {Fluid-Attenuated Inversion Recovery Vascular Hyperintensities: An Important Imaging Marker for Cerebrovascular Disease},
	url = {http://www.ajnr.org/content/32/10/1771},
	volume = {32},
	year = {2011}
}

@article{FLAIRFirst,
	author = {Hajnal, Joseph and {De Coene}, Beatrice and {D. Lewis}, Paul and {J. Baudouin}, Christine and {M. Cowan}, Frances and {M. Pennock}, Jacqueline and {R. Young}, Ian and {M. Bydder}, Graeme},
	booktitle = {Journal of Computer Assisted Tomography - J COMPUT ASSIST TOMOGR},
	month = {07},
	pages = {506--513},
	title = {High Signal Regions in Normal White Matter Shown by Heavily T2Weighted CSF Nulled IR Sequences},
	volume = {16},
	year = {1992}
}

@article{Leukoaraiosis,
	abstract = {Leukoaraiosis is a common finding in stroke patients and has been strongly associated with risk of incident stroke and dementia. Leukoaraiosis may also be an independent predictor of stroke outcomes. There is increasing evidence from neuroimaging to support the concept that some leukoaraiosis is caused by white matter infarcts, which may be particularly frequent in patients with aggressive small vessel diseases such as cerebral amyloid angiopathy. The relatively similar distribution of leukoaraiosis regardless of the distribution of vascular pathology suggests a conserved vulnerability to white matter injury across various vascular diseases, possibly related to resting patterns of blood flow. More insights into the pathophysiology of leukoaraiosis are sorely needed to reduce the burden of disability associated with this common condition.},
	author = {{Smith Eric E.}},
	journal = {Stroke},
	month = oct,
	number = {10 suppl 1},
	pages = {S139},
	risfield_0_lp = {S143},
	risfield_1_m3 = {10.1161/STROKEAHA.110.596056},
	title = {Leukoaraiosis and Stroke},
	volume = {41},
	year = {2010}
}

@article{ToFOriginal,
	author = {Dumoulin, C. L. and Cline, H. E. and Souza, S. P. and Wagle, W. A. and Walker, M. F.},
	doi = {10.1002/mrm.1910110104},
	issn = {1522-2594},
	journal = {Magnetic Resonance in Medicine},
	number = {1},
	pages = {35--46},
	publisher = {Wiley Subscription Services, Inc., A Wiley Company},
	title = {Three-dimensional time-of-flight magnetic resonance angiography using spin saturation},
	url = {http://dx.doi.org/10.1002/mrm.1910110104},
	volume = {11},
	year = {1989}
}

@article{ToFMultislab,
	author = {Parker, Dennis L. and Yuan, Chun and Blatter, Duane D.},
	doi = {10.1002/mrm.1910170215},
	issn = {1522-2594},
	journal = {Magnetic Resonance in Medicine},
	number = {2},
	pages = {434--451},
	publisher = {Wiley Subscription Services, Inc., A Wiley Company},
	title = {MR angiography by multiple thin slab 3D acquisition},
	url = {http://dx.doi.org/10.1002/mrm.1910170215},
	volume = {17},
	year = {1991}
}

@article{FLAIRHSV,
	abstract = {Objective: To describe hyperintense vessels sign (HVS) in patients with acute stroke on fluid-attenuated inversion recovery (FLAIR) MRI and determine its clinical significance and utility. Background: Enhancement of vessels on postcontrast MRI in patients with acute stroke is considered an indicator of early brain ischemia. Recently, the FLAIR technique has shown promise in earlier and better detection of ischemic brain parenchymal lesions. Methods: Two observers retrospectively reviewed 304 MRI of patients with stroke and identified 30 patients with acute middle cerebral artery stroke and HVS on FLAIR obtained within 24 hours of symptom onset. These patients were evaluated with contrast-enhanced MRI (n = 9), MR angiography of carotid and intracranial circulation (n = 30), cerebral angiography (n = 8), transcranial Doppler (n = 17), and SPECT (n = 16). The extent of HVS was compared with final infarct size and NIH Stroke Scale score. Results: HVS on FLAIR was seen in 10\% of the patients with acute stroke. HVS was associated with large vessel occlusion or severe stenosis (\&gt;90\%). Intravascular enhancement on contrast MRI was observed in vessels that were hyperintense on FLAIR. Both cortical and subcortical infarcts demonstrated HVS. MR angiographic and cerebral angiographic findings of large vessel occlusion or severe stenosis (\&gt;90\%), slow flow, low velocities by transcranial Doppler, and hypoperfusion on SPECT correlated with HVS. HVS was the earliest ischemic change in three patients scanned within 3 hours of ictus. Final infarct size was smaller than the area showing HVS in all patients. Conclusion: HVS on FLAIR MRI is an indicator of slow flow and early ischemia as a result of large vessel occlusion or stenosis and inadequacy of collateral circulation. HVS does not mean that infarction has occurred but indicates brain tissue at risk of infarction. It should prompt consideration of revascularization and flow augmentation strategies.},
	author = {Kamran, S. and Bates, V. and Bakshi, R. and Wright, P. and Kinkel, W. and Miletich, R.},
	doi = {10.1212/WNL.55.2.265},
	eprint = {http://n.neurology.org/content/55/2/265.full.pdf},
	issn = {0028-3878},
	journal = {Neurology},
	number = {2},
	pages = {265--269},
	publisher = {American Academy of Neurology},
	title = {Significance of hyperintense vessels on FLAIR MRI in acute stroke},
	url = {http://n.neurology.org/content/55/2/265},
	volume = {55},
	year = {2000}
}

@article{TissueSalvageTime,
	author = {{Baron Jean-Claude} and {Moseley Michael E.}},
	doi = {10.1053/jscd.2000.18910; 10.1053/jscd.2000.18910},
	issn = {1052-3057},
	journal = {Journal of Stroke and Cerebrovascular Diseases},
	month = feb,
	note = {doi: 10.1053/jscd.2000.18910},
	number = {6},
	pages = {15--20},
	publisher = {Elsevier},
	risfield_0_t2 = {Journal of Stroke and Cerebrovascular Diseases},
	risfield_1_m3 = {doi: 10.1053/jscd.2000.18910},
	title = {For how long is brain tissue salvageable? Imaging-based evidence},
	volume = {9},
	year = {2018}
}

@article{TimeToInfarctSwelling,
	abstract = { Background:The risk of symptomatic infarct swelling has been reported to be higher in patients treated with recombinant tissue plasminogen activator (rt-PA). The aim of this study was to evaluate the timing of symptomatic infarct swelling after rt-PA treatment.Methods:We retrospectively analyzed 14 868 patients with acute ischemic stroke from a stroke registry databank. We recruited patients with massive middle cerebral artery (MCA) infarction and symptomatic infarct swelling and excluded those with parenchymal or symptomatic hemorrhage. Multiple linear regression and multivariate logistic regression analyses were used to estimate the impact of rt-PA on the timing of symptomatic infarct swelling.Results:A total of 23 patients with rt-PA treatment and 117 patients without rt-PA treatment were included. The rt-PA treatment group had a lower rate of coronary artery disease (8.7\% vs 32.5\%; P = .023), lower severity of baseline National Institutes of Health Stroke Scale score (19 vs 23; P = .014), shorter duration of infarct swelling (27.6 vs 45.4 hours; P < .001), and higher rate of hemicraniectomy surgery (65.2\% vs 28.2\%; P =.001) than those without rt-PA treatment. After adjusting for variables in multiple linear regression analysis, rt-PA treatment and an elevated C-reactive protein level were associated with early symptomatic infarct swelling (P = .014 and P = .041, respectively). The rt-PA treatment was an independent factor related to early symptomatic infarct swelling within 36 hours (P = .005; odds ratio [OR]: 5.3; confidence interval [CI]: 1.65-17.0) or 48 hours (P = .009; OR: 16.4; CI: 2.00-134).Conclusion:Intravenous rt-PA treatment may hasten the onset of cerebral edema and subsequent cerebral herniation in large MCA territory infarction. },
	author = {Lin, Ting-Chun and Lee, Jiann-Der and Lin, Ya-Hui and Yuan, Rey-Yue and Weng, Hsu-Huei and Huang, Ying-Chih and Lee, Meng and Wu, Chih-Ying and Hsu, Huan-Lin and Hsu, Chia-Yu and Lee, Tsong-Hai and Liu, Shan-Jin and Chang, Yeu-Jhy and Chang, Chien-Hung and Peng, Tsung-I and Liou, Chia-Wei and Chang, Ku-Chou and Pan, Yi-Ting and Huang, Yen-Chu},
	doi = {10.1177/1076029616659693},
	eprint = {https://doi.org/10.1177/1076029616659693 },
	journal = {Clinical and Applied Thrombosis/Hemostasis},
	note = {PMID: 27443695},
	number = {7},
	pages = {814--820},
	title = {Timing of Symptomatic Infarct Swelling Following Intravenous Thrombolysis in Acute Middle Cerebral Artery Infarction: A Case--Control Study},
	url = { 
        https://doi.org/10.1177/1076029616659693
    
},
	volume = {23},
	year = {2017}
}

@article{InfarctSwellingDamage,
	author = {{Dohmen C.} and {Galldiks N.} and {Bosche B.} and {Kracht L.} and {Graf R.}},
	doi = {10.1159/000330648},
	issn = {1015-9770},
	journal = {Cerebrovasc Dis},
	number = {1},
	pages = {1--7},
	title = {The Severity of Ischemia Determines and Predicts Malignant Brain Edema in Patients with Large Middle Cerebral Artery Infarction},
	volume = {33},
	year = {2012}
}

@article{DecompressiveTherapy,
	author = {B., Taylor and M., Lopresti and G., Appelboom and E., Jr., Sander Connolly},
	journal = {JOURNAL OF NEUROSURGICAL SCIENCES},
	month = mar,
	number = {59(1)},
	pages = {73--8},
	title = {Hemicraniectomy for malignant middle cerebral artery territory infarction: an updated review},
	year = {2015}
}

@article{rTPA,
	author = {{Hacke Werner} and {Kaste Markku} and {Bluhmki Erich} and {Brozman Miroslav} and {D{\'a}valos Antoni} and {Guidetti Donata} and {Larrue Vincent} and {Lees Kennedy R.} and {Medeghri Zakaria} and {Machnig Thomas} and {Schneider Dietmar} and von R{\"u}diger, Kummer and {Wahlgren Nils} and {Toni Danilo}},
	doi = {10.1056/NEJMoa0804656; 10.1056/NEJMoa0804656},
	issn = {0028-4793},
	journal = {New England Journal of Medicine},
	note = {doi: 10.1056/NEJMoa0804656},
	number = {13},
	pages = {1317--1329},
	publisher = {Massachusetts Medical Society},
	risfield_0_da = {2008/09/25},
	risfield_1_t2 = {New England Journal of Medicine},
	risfield_2_m3 = {doi: 10.1056/NEJMoa0804656},
	title = {Thrombolysis with Alteplase 3 to 4.5 Hours after Acute Ischemic Stroke},
	volume = {359},
	year = {2008}
}

@article{thrombolysisHaemorrhage,
	author = {Yaghi, S and Eisenberger, A and JZ, Willey},
	doi = {10.1001/jamaneurol.2014.1210},
	eprint = {/data/journals/neur/930724/nrv140004.pdf},
	journal = {JAMA Neurology},
	number = {9},
	pages = {1181--1185},
	title = {Symptomatic intracerebral hemorrhage in acute ischemic stroke after thrombolysis with intravenous recombinant tissue plasminogen activator: A review of natural history and treatment},
	url = { + http://dx.doi.org/10.1001/jamaneurol.2014.1210},
	volume = {71},
	year = {2014}
}

@article{CollateralOutcome,
	abstract = {Background Few data are available on the relationship between fluid-attenuated inversion recovery vascular hyperintensities and proximal middle cerebral artery occlusion prognosis.Objectives To assess a fluid-attenuated inversion recovery vascular hyperintensities score (FVHS) and explore its relationship with recanalization status and clinical outcomes after intravenous thrombolysis.Design Retrospective study.Setting Stroke unit in a university hospital.Patients Consecutive patients with proximal middle cerebral artery occlusion, thrombolysed within 6 hours, were selected from our prospective database. The FVHS (range, 0-10; divided into low, medium, and high thirds) was quantified on the magnetic resonance image obtained at admission. Recanalization rates, infarction size (Alberta Stroke Program Early CT Score applied to diffusion-weighted imaging [ASPECTS-DWI]), and 3-month functional outcomes (modified Rankin Scale score) were determined. Poor outcomes and large infarctions were defined as a modified Rankin Scale score higher than 2 and an ASPECTS-DWI score of 5 or lower, respectively.Main Outcome Measures Interaction among FVHS, recanalization status, and outcomes.Results Thirty-four patients had a low FVHS (\ensuremath{\leq}4), 32 had a medium FVHS (5 or 6), and 39 had a high FVHS (\ensuremath{\geq}7). The rate of poor functional outcome (modified Rankin Scale score \&gt;2) was higher for the group with low FVHSs than those with medium FVHSs and high FVHSs (82.3\% vs 43.7\% and 43.5\%, respectively; P \&lt; .001). The rate of 24-hour large infarctions (ASPECTS-DWI score \ensuremath{\leq}5) was higher for those with low FVHSs than those with medium and high FVHSs (88.2\% vs 56.2\% and 51.3\%, respectively; P = .002). The recanalization rate was not associated with FVHS. Multivariate analysis retained low FVHS as an independent early predictor of poor clinical outcome (odds ratio = 9.91; 95\% CI, 2.01-48.93; P = .004) and large infarction (odds ratio = 6.99; 95\% CI, 1.78-27.46; P = .005). Low FVHS remained associated with poor outcomes regardless of recanalization status. Early recanalization in patients with a low FVHS decreased the poor functional outcome rate from 100\% to 64.7\% (P = .02).Conclusions The FVHS is an early independent prognostic marker for patients with proximal middle cerebral artery occlusion. Synergy between FVHS and recanalization status appears to be a critical determinant of final outcomes, supporting intensive reperfusion treatment for patients with a low FVHS.},
	author = {Olindo, S and Chausson, N and Joux, J and et al},
	doi = {10.1001/archneurol.2012.1310},
	issn = {0003-9942},
	journal = {Archives of Neurology},
	month = nov,
	note = {10.1001/archneurol.2012.1310},
	number = {11},
	pages = {1462--1468},
	risfield_0_m3 = {doi: 10.1001/archneurol.2012.1310},
	title = {Fluid-attenuated inversion recovery vascular hyperintensity: An early predictor of clinical outcome in proximal middle cerebral artery occlusion},
	volume = {69},
	year = {2012}
}

@article{PermeableThrombus,
	abstract = {Background and Purpose---Preclinical studies showed that thrombus permeability improves recombinant tissue-type plasminogen activator (r-tPA) efficacy. We hypothesize that thrombus permeability estimated from radiological imaging is associated with improved recanalization after treatment with intravenously administered r-tPA (r-tPA) and with better functional outcome.Methods---We assessed thrombus attenuation increase (TAI) in patients from the Dutch Acute Stroke Study with an occlusion of an intracranial artery on computed tomographic angiography. Patients were included within 9 hours after the stroke onset. After dichotomization of TAI as pervious or impervious, logistic regressions analyses were performed to estimate associations of intravenous r-tPA therapy with complete recanalization and with favorable functional outcome (modified Rankin Scale score of \ensuremath{\leq}2).Results---Three hundred eight patients matched the inclusion criteria. The median TAI was 20.1 (interquartile range, 8.5--37.8) Hounsfield unit (HU). We found a significant increase in the odds of complete recanalization with increasing TAI for patients treated with intravenous r-tPA (P=0.030). One hundred thirty-one (42\%) thrombi were classified as pervious with TAI of \ensuremath{\geq}23 HU. In patients with a pervious thrombus, complete recanalization was more frequent after treatment with intravenous r-tPA than after conservative treatment (odds ratio, 6.26; 95\% confidence interval, 2.4--16.8; P\&amp;lt;0.001). In patients with an impervious thrombus, the effect of intravenous r-tPA was not significant (odds ratio, 1.4; 95\% confidence interval, 0.5--4.1; P=0.47). Favorable outcome was more common in patients with a pervious thrombi than without (odds ratio, 2.1; 95\% confidence interval, 1.3--3.4; P=0.001).Conclusions---Thrombus perviousness, as measured on computed tomography in the acute stage of ischemic stroke, is strongly associated with recanalization after intravenous r-tPA treatment and with favorable functional outcome.},
	author = {{Santos Emilie M.M.} and {Dankbaar Jan Willem} and {Treurniet Kilian M.} and {Horsch Alexander D.} and {Roos Yvo B.} and {Kappelle L. Jaap} and {Niessen Wiro J.} and {Majoie Charles B.} and {Velthuis Birgitta} and {Marquering Henk A.}},
	journal = {Stroke},
	month = aug,
	number = {8},
	pages = {2058},
	risfield_0_lp = {2065},
	risfield_1_m3 = {10.1161/STROKEAHA.116.013306},
	title = {Permeable Thrombi Are Associated With Higher Intravenous Recombinant Tissue-Type Plasminogen Activator Treatment Success in Patients With Acute Ischemic Stroke},
	url = {http://stroke.ahajournals.org/content/47/8/2058.abstract},
	volume = {47},
	year = {2016}
}

@article{DoubleTIV,
	abstract = {Background and Purpose---Intravenous tissue-type plasminogen activator (IV tPA) frequently fails to recanalize proximal middle cerebral artery (MCA-M1) obstructions, preventing favorable outcomes. Only neurointerventional procedures prevail in these cases, but well-equipped centers remain scarce. A new therapeutic strategy consisting of a second IV thrombolysis with low-dose tenecteplase was applied. Methods---Consecutive patients with an MCA-M1 occlusion that did not reopen at the end of IV tPA perfusion received IV tenecteplase (0.1 mg/kg). Partial or complete thrombolysis in myocardial infarction recanalization (Thrombolysis In Myocardial Infarction grade 2/3) and intracerebral hemorrhage were assessed by magnetic resonance imaging ≈24 hours later. Clinical outcomes at 3 months were evaluated with the modified Rankin score. Results---Among 40 patients with MCA-M1 occlusions who received IV tPA, 13 were treated according to the protocol of sequential combined IV thrombolytics. Baseline National Institutes of Health Stroke Scale score was 15. At a mean of 16.8 hours after IV thrombolysis, the recanalization rate was 100\% (2 with Thrombolysis In Myocardial Infarction grade 2, 11 with Thrombolysis In Myocardial Infarction grade 3). Intracerebral hemorrhage occurred in 4 of 13 (31\%) patients, with no symptomatic hemorrhage. Good clinical outcomes (modified Rankin score=0/1) were achieved in 9 of 13 (69\%) patients. Functional outcomes were very similar to those of 13 patients with early IV-tPA recanalization. Among 4 patients treated as protocol violations, 1 presented with a lack of recanalization and a parenchymal hematoma type 2. Conclusions---For patients with MCA-M1 occlusions treated with IV tPA but without early recanalization, a second bolus of IV tenecteplase (0.1 mg/kg) may be a relatively safe, effective, and easy option in carefully selected cases, but additional studies are needed to confirm these findings.},
	author = {{Smadja Didier} and {Chausson Nicolas} and {Joux Julien} and {Saint-Vil Martine} and {Signat{\'e} A{\"\i}ssatou} and {Edimonana Mireille} and {Jeannin S{\'e}verine} and {Bartoli Blaise} and {Aveillan Mathieu} and {Cabre Philippe} and {Olindo St{\'e}phane}},
	journal = {Stroke},
	month = jun,
	number = {6},
	pages = {1644},
	risfield_0_lp = {1647},
	risfield_1_m3 = {10.1161/STROKEAHA.110.610147},
	title = {A New Therapeutic Strategy for Acute Ischemic Stroke},
	url = {http://stroke.ahajournals.org/content/42/6/1644.abstract},
	volume = {42},
	year = {2011}
}

@article{ThrombectomyMethods,
	abstract = {Importance The benefits of endovascular revascularization using the contact aspiration technique vs the stent retriever technique in patients with acute ischemic stroke remain uncertain because of lack of evidence from randomized trials.Objective To compare efficacy and adverse events using the contact aspiration technique vs the standard stent retriever technique as a first-line endovascular treatment for successful revascularization among patients with acute ischemic stroke and large vessel occlusion.Design, Setting, and Participants The Contact Aspiration vs Stent Retriever for Successful Revascularization (ASTER) study was a randomized, open-label, blinded end-point clinical trial conducted in 8 comprehensive stroke centers in France (October 2015-October 2016). Patients who presented with acute ischemic stroke and a large vessel occlusion in the anterior circulation within 6 hours of symptom onset were included.Interventions Patients were randomly assigned to first-line contact aspiration (n = 192) or first-line stent retriever (n = 189) immediately prior to mechanical thrombectomy.Main Outcomes and Measures The primary outcome was the proportion of patients with successful revascularization defined as a modified Thrombolysis in Cerebral Infarction score of 2b or 3 at the end of all endovascular procedures. Secondary outcomes included degree of disability assessed by overall distribution of the modified Rankin Scale (mRS) score at 90 days, change in National Institutes of Health Stroke Scale (NIHSS) score at 24 hours, all-cause mortality at 90 days, and procedure-related serious adverse events.Results Among 381 patients randomized (mean age, 69.9 years; 174 women [45.7\%]), 363 (95.3\%) completed the trial. Median time from symptom onset to arterial puncture was 227 minutes (interquartile range, 180-280 minutes). For the primary outcome, the proportion of patients with successful revascularization was 85.4\% (n = 164) in the contact aspiration group vs 83.1\% (n = 157) in the stent retriever group (odds ratio, 1.20 [95\% CI, 0.68-2.10]; P = .53; difference, 2.4\% [95\% CI, −5.4\% to 9.7\%]). For the clinical efficacy outcomes (change in NIHSS score at 24 hours, mRS score at 90 days) and adverse events, there were no significant differences between groups.Conclusions and Relevance Among patients with ischemic stroke in the anterior circulation undergoing thrombectomy, first-line thrombectomy with contact aspiration compared with stent retriever did not result in an increased successful revascularization rate at the end of the procedure.Trial Registration clinicaltrials.gov Identifier: NCT02523261},
	author = {Lapergue, B and Blanc, R and Gory, B and et al},
	doi = {10.1001/jama.2017.9644},
	issn = {0098-7484},
	journal = {JAMA},
	month = aug,
	note = {10.1001/jama.2017.9644},
	number = {5},
	pages = {443--452},
	risfield_0_m3 = {doi: 10.1001/jama.2017.9644},
	title = {Effect of endovascular contact aspiration vs stent retriever on revascularization in patients with acute ischemic stroke and large vessel occlusion: The aster randomized clinical trial},
	volume = {318},
	year = {2017}
}

@article{ThrombectomySuccessRate,
	author = {{Goyal Mayank} and {Menon Bijoy K} and van Zwam Wim, H and {Dippel Diederik W J} and {Mitchell Peter J} and {Demchuk Andrew M} and {D{\'a}valos Antoni} and {Majoie Charles B L M} and van der Aad, Lugt and de Miquel Maria, A and {Donnan Geoffrey A} and {Roos Yvo B W E M} and {Bonafe Alain} and {Jahan Reza} and {Diener Hans-Christoph} and van den Berg Lucie, A and {Levy Elad I} and {Berkhemer Olvert A} and {Pereira Vitor M} and {Rempel Jeremy} and {Mill{\'a}n M{\`o}nica} and {Davis Stephen M} and {Roy Daniel} and {Thornton John} and {Rom{\'a}n Luis San} and {Rib{\'o} Marc} and {Beumer Debbie} and {Stouch Bruce} and {Brown Scott} and {Campbell Bruce C V} and van Oostenbrugge Robert, J and {Saver Jeffrey L} and {Hill Michael D} and {Jovin Tudor G}},
	doi = {10.1016/S0140-6736(16)00163-X; 10.1016/S0140-6736(16)00163-X},
	issn = {0140-6736},
	journal = {The Lancet},
	month = feb,
	note = {doi: 10.1016/S0140-6736(16)00163-X},
	number = {10029},
	pages = {1723--1731},
	publisher = {Elsevier},
	risfield_0_t2 = {The Lancet},
	risfield_1_m3 = {doi: 10.1016/S0140-6736(16)00163-X},
	title = {Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials},
	volume = {387},
	year = {2018}
}

@article{DistalThrombectomy,
	abstract = {BACKGROUND AND PURPOSE: Patients with acute ischemic stroke in the anterior circulation are at risk for either primary or, following mechanical thrombectomy, secondary occlusion of the anterior cerebral artery. Because previous studies had only a limited informative value, we report our data concerning the frequency and location of distal anterior cerebral artery occlusions, recanalization rates, periprocedural complications, and clinical outcome.MATERIALS AND METHODS: We performed a retrospective analysis of prospectively collected data of patients with acute ischemic stroke undergoing mechanical thrombectomy in the anterior circulation between June 2010 and April 2015.RESULTS: Of 368 patients included in this analysis, we identified 30 (8.1\%) with either primary (n = 17, 4.6\%) or secondary (n = 13, 3.5\%) embolic occlusion of the distal anterior cerebral artery. The recanalization rate after placement of a stent retriever was 88\%. Periprocedural complications were rare and included vasospasms (n = 3, 10\%) and dissection (n = 1, 3.3\%). However, 16 (53.5\%) patients sustained an (at least partial) infarction of the anterior cerebral artery territory. Ninety days after the ictus, clinical outcome according to the modified Rankin Scale score was the following: 0--2, n = 11 (36.6\%); 3--4, n = 9 (30\%); 5--6, n = 10 (33.3\%).CONCLUSIONS: Occlusions of the distal anterior cerebral artery affect approximately 8\% of patients with acute ischemic stroke in the anterior circulation receiving mechanical thrombectomy. Despite a high recanalization rate and a low complication rate, subsequent (partial) infarction in the anterior cerebral artery territory occurs in approximately half of patients. Fortunately, clinical outcome appears not to be predominately unfavorable.ACAanterior cerebral arteryMTmechanical thrombectomy},
	author = {{Pfaff J.} and {Herweh C.} and {Pham M.} and {Schieber S.} and {Ringleb P.A.} and {Bendszus M.} and {M{\"o}hlenbruch M.}},
	journal = {American Journal of Neuroradiology},
	month = apr,
	number = {4},
	pages = {673},
	risfield_0_lp = {678},
	risfield_1_m3 = {10.3174/ajnr.A4594},
	title = {Mechanical Thrombectomy of Distal Occlusions in the Anterior Cerebral Artery: Recanalization Rates, Periprocedural Complications, and Clinical Outcome},
	url = {http://www.ajnr.org/content/37/4/673.abstract},
	volume = {37},
	year = {2016}
}

@article{StrokeGuidelines,
	abstract = {Background and Purpose---The purpose of these guidelines is to provide an up-to-date comprehensive set of recommendations for clinicians caring for adult patients with acute arterial ischemic stroke in a single document. The intended audiences are prehospital care providers, physicians, allied health professionals, and hospital administrators. These guidelines supersede the 2013 guidelines and subsequent updates.Methods---Members of the writing group were appointed by the American Heart Association Stroke Council{\rq}s Scientific Statements Oversight Committee, representing various areas of medical expertise. Strict adherence to the American Heart Association conflict of interest policy was maintained. Members were not allowed to participate in discussions or to vote on topics relevant to their relations with industry. The members of the writing group unanimously approved all recommendations except when relations with industry precluded members voting. Prerelease review of the draft guideline was performed by 4 expert peer reviewers and by the members of the Stroke Council{\rq}s Scientific Statements Oversight Committee and Stroke Council Leadership Committee. These guidelines use the American College of Cardiology/American Heart Association 2015 Class of Recommendations and Levels of Evidence and the new American Heart Association guidelines format.Results---These guidelines detail prehospital care, urgent and emergency evaluation and treatment with intravenous and intra-arterial therapies, and in-hospital management, including secondary prevention measures that are appropriately instituted within the first 2 weeks. The guidelines support the overarching concept of stroke systems of care in both the prehospital and hospital settings.Conclusions---These guidelines are based on the best evidence currently available. In many instances, however, only limited data exist demonstrating the urgent need for continued research on treatment of acute ischemic stroke.},
	author = {{Powers William J.} and {Rabinstein Alejandro A.} and {Ackerson Teri} and {Adeoye Opeolu M.} and {Bambakidis Nicholas C.} and {Becker Kyra} and {Biller Jos{\'e}} and {Brown Michael} and {Demaerschalk Bart M.} and {Hoh Brian} and {Jauch Edward C.} and {Kidwell Chelsea S.} and {Leslie-Mazwi Thabele M.} and {Ovbiagele Bruce} and {Scott Phillip A.} and {Sheth Kevin N.} and {Southerland Andrew M.} and {Summers Deborah V.} and {Tirschwell David L.}},
	journal = {Stroke},
	month = jan,
	number = {3},
	risfield_0_m3 = {10.1161/STR.0000000000000158},
	title = {2018 Guidelines for the Early Management of Patients With Acute Ischemic Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association},
	url = {http://stroke.ahajournals.org/content/early/2018/01/23/STR.0000000000000158.abstract},
	volume = {49},
	year = {2018}
}

@article{HaemorrhageRisk,
	abstract = {Background: In order to determine the impact of the severity of ischemia on malignant edema formation, we investigated various degrees of perfusional deficit by <sup>11</sup>C-flumazenil PET in patients with large middle cerebral artery (MCA) infarction. Methods: 17 patients with large MCA stroke were included. Cerebral blood flow (CBF) was measured 15.9 {\textpm} 6.4 h after the ictus. Patients were divided into a malignant (n = 9) and a benign group (n = 8) as a function of their clinical courses and edema. Edema was measured as maximal midline shift on follow-up CTs. Total hypoperfusion volume was divided into different subvolumes according to the degree of CBF reduction. Results: Subvolumes of severe ischemia relative to total ischemic area were significantly larger in the malignant group than in the benign group and were significantly correlated with edema formation. The highest correlation and best predictive values for edema formation with a sensitivity, specificity, and a positive and negative predictive value of 100\% were found for subvolumes with severe ischemia. Correlation coefficients and prediction decreased for subvolumes with less severe perfusional deficit, pointing to the risk of misclassifying patients when relying on the volume of total perfusional deficit alone. Conclusions: Malignant MCA infarction seems to be determined more by the volume of severe perfusional deficit than that of total perfusional deficit. Assessment of severely ischemic areas allows prediction of malignant edema formation and might help to select candidates for hemicraniectomy.},
	author = {{Dohmen C.} and {Galldiks N.} and {Bosche B.} and {Kracht L.} and {Graf R.}},
	doi = {10.1159/000330648},
	issn = {1015-9770},
	journal = {Cerebrovasc Dis},
	number = {1},
	pages = {1--7},
	title = {The Severity of Ischemia Determines and Predicts Malignant Brain Edema in Patients with Large Middle Cerebral Artery Infarction},
	volume = {33},
	year = {2012}
}

@article{MRIIntensityStandardNyu,
	author = {Nyul, L. G. and Udupa, J. K. and Zhang, Xuan},
	doi = {10.1109/42.836373},
	issn = {0278-0062},
	journal = {IEEE Transactions on Medical Imaging},
	keywords = {biomedical MRI; medical image processing; MR scanner brand; MRI scale standardization method; abnormalities; absolute intensity values; body region; fixed meaning; healthy organs; image display; image normalization; magnetic resonance imaging; medical diagnostic imaging; quantitative information extraction; scanner-dependent variations; tissue meaning; two-step method; Body regions; Computed tomography; Data mining; Displays; Humans; Image segmentation; Magnetic resonance imaging; Protocols; Standardization; X-ray imaging; Algorithms; Brain; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Multiple Sclerosis},
	month = {Feb},
	number = {2},
	pages = {143--150},
	title = {New variants of a method of MRI scale standardization},
	volume = {19},
	year = {2000}
}

@article{Watershed-2,
	author = {{Meyer Fernand}},
	doi = {10.1016/0165-1684(94)90060-4},
	issn = {0165-1684},
	journal = {Signal Processing},
	keywords = {Topographic distance; Watershed line; Segmentation; Shortest path algorithms; Mathematical morphology},
	number = {1},
	pages = {113--125},
	risfield_0_da = {1994/07/01/},
	risfield_1_t2 = {Mathematical Morphology and its Applications to Signal Processing},
	title = {Topographic distance and watershed lines},
	url = {http://www.sciencedirect.com/science/article/pii/0165168494900604},
	volume = {38},
	year = {1994}
}

@article{NyulEqui,
	author = {Knight, Jesse and Taylor, Graham W. and Khademi, April},
	journal = {Journal of Computational Vision and Imaging Systems},
	number = {1},
	title = {Equivalence of histogram equalization, histogram matching and the nyul algorithm for intensity standardization in MRI},
	volume = {3},
	year = {2017}
}

@article{otsu,
	author = {Otsu, N.},
	doi = {10.1109/TSMC.1979.4310076},
	issn = {0018-9472},
	journal = {IEEE Transactions on Systems, Man, and Cybernetics},
	keywords = {Displays; Gaussian distribution; Histograms; Least squares approximation; Marine vehicles; Q measurement; Radar tracking; Sea measurements; Surveillance; Target tracking},
	number = {1},
	pages = {62--66},
	risfield_0_t2 = {IEEE Transactions on Systems, Man, and Cybernetics},
	risfield_1_vo = {9},
	title = {A Threshold Selection Method from Gray-Level Histograms},
	volume = {9},
	year = {1979}
}

@article{dice,
	author = {{Dice Lee R.}},
	doi = {10.2307/1932409},
	issn = {00129658, 19399170},
	journal = {Ecology},
	number = {3},
	pages = {297--302},
	publisher = {Ecological Society of America},
	risfield_0_c1 = {Full publication date: Jul., 1945},
	risfield_1_t2 = {Ecology},
	title = {Measures of the Amount of Ecologic Association Between Species},
	url = {http://www.jstor.org/stable/1932409},
	volume = {26},
	year = {1945}
}

@article{ISLES2015,
	author = {{Maier Oskar} and {Menze Bjoern H.} and von der Janina, Gablentz and {H{\"a}ni Levin} and {Heinrich Mattias P.} and {Liebrand Matthias} and {Winzeck Stefan} and {Basit Abdul} and {Bentley Paul} and {Chen Liang} and {Christiaens Daan} and {Dutil Francis} and {Egger Karl} and {Feng Chaolu} and {Glocker Ben} and {G{\"o}tz Michael} and {Haeck Tom} and {Halme Hanna-Leena} and {Havaei Mohammad} and {Iftekharuddin Khan M.} and {Jodoin Pierre-Marc} and {Kamnitsas Konstantinos} and {Kellner Elias} and {Korvenoja Antti} and {Larochelle Hugo} and {Ledig Christian} and {Lee Jia-Hong} and {Maes Frederik} and {Mahmood Qaiser} and {Maier-Hein Klaus H.} and {McKinley Richard} and {Muschelli John} and {Pal Chris} and {Pei Linmin} and {Rangarajan Janaki Raman} and {Reza Syed M.S.} and {Robben David} and {Rueckert Daniel} and {Salli Eero} and {Suetens Paul} and {Wang Ching-Wei} and {Wilms Matthias} and {Kirschke Jan S.} and {Kr{\"a}mer Ulrike M.} and {M{\"u}nte Thomas F.} and {Schramm Peter} and {Wiest Roland} and {Handels Heinz} and {Reyes Mauricio}},
	doi = {10.1016/j.media.2016.07.009},
	issn = {1361-8415},
	journal = {Medical Image Analysis},
	keywords = {Ischemic stroke; Segmentation; MRI; Challenge; Benchmark; Comparison},
	pages = {250--269},
	risfield_0_da = {2017/01/01/},
	title = {ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI},
	url = {http://www.sciencedirect.com/science/article/pii/S1361841516301268},
	volume = {35},
	year = {2017}
}

@article{LesionCNN,
	author = {{Chen Liang} and {Bentley Paul} and {Rueckert Daniel}},
	doi = {10.1016/j.nicl.2017.06.016},
	issn = {2213-1582},
	journal = {NeuroImage: Clinical},
	keywords = {Acute ischemic lesion segmentation; DWI; Deep learning; Convolutional neural networks},
	pages = {633--643},
	risfield_0_da = {2017/01/01/},
	title = {Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks},
	url = {http://www.sciencedirect.com/science/article/pii/S221315821730147X},
	volume = {15},
	year = {2017}
}

@article{LesionZeta,
	abstract = {Making robust inferences about the functional neuroanatomy of the brain is critically dependent on experimental techniques that examine the consequences of focal loss of brain function. Unfortunately, the use of the most comprehensive such technique---lesion-function mapping---is complicated by the need for time-consuming and subjective manual delineation of the lesions, greatly limiting the practicability of the approach. Here we exploit a recently-described general measure of statistical anomaly, zeta, to devise a fully-automated, high-dimensional algorithm for identifying the parameters of lesions within a brain image given a reference set of normal brain images. We proceed to evaluate such an algorithm in the context of diffusion-weighted imaging of the commonest type of lesion used in neuroanatomical research: ischaemic damage. Summary performance metrics exceed those previously published for diffusion-weighted imaging and approach the current gold standard---manual segmentation---sufficiently closely for fully-automated lesion-mapping studies to become a possibility. We apply the new method to 435 unselected images of patients with ischaemic stroke to derive a probabilistic map of the pattern of damage in lesions involving the occipital lobe, demonstrating the variation of anatomical resolvability of occipital areas so as to guide future lesion-function studies of the region.},
	author = {{Mah Yee-Haur} and {Jager Rolf} and {Kennard Christopher} and {Husain Masud} and {Nachev Parashkev}},
	doi = {10.1016/j.cortex.2012.12.008},
	issn = {0010-9452 1973-8102},
	journal = {Cortex; a Journal Devoted to the Study of the Nervous System and Behavior},
	month = dec,
	number = {100},
	pages = {51--63},
	publisher = {Masson},
	risfield_0_db = {PMC},
	risfield_1_u1 = {S0010-9452(12)00343-7[PII]},
	risfield_2_an = {PMC4071441},
	risfield_3_u1 = {23347558[pmid]},
	title = {A new method for automated high-dimensional lesion segmentation evaluated in vascular injury and applied to the human occipital lobe},
	url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4071441/},
	volume = {56},
	year = {2012}
}

@inproceedings{DWILesionFUZZYC2,
	author = {Muda, A. F. and Saad, N. M. and Waeleh, N. and Abdullah, A. R. and Fen, L. Y.},
	booktitle = {2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)},
	doi = {10.1109/AIMS.2015.88},
	keywords = {biomedical MRI; brain; fuzzy set theory; image segmentation; medical image processing; tumours; Dice indices; Jaccard indices; active contour; acute stroke; brain lesion automatic detection; brain lesion segmentation; chronic stroke; correlation template; diffusion-weighted MRI; diffusion-weighted magnetic resonance images; false negative rate; false positive rate; fuzzy C-means integration; hyperintense lesions; hypointense lesions; necrosis; solid tumor; Active contours; Clustering algorithms; Correlation; Image segmentation; Lesions; Magnetic resonance imaging; diffusion-weighted imaging (DWI); segmentation; Fuzzy C-Means; template matching; active contour.},
	pages = {268--273},
	risfield_0_t2 = {2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)},
	title = {Integration of Fuzzy C-Means with Correlation Template and Active Contour for Brain Lesion Segmentation in Diffusion-Weighted MRI},
	year = {2015}
}

@inproceedings{DWILesionFUZZYC1,
	author = {Tsai, Jang-Zern and Peng, Syu-Jyun and Chen, Yu-Wei Roy and Wang, Kuo-Wei and Wu, Hsiao-kuang and Lin, Yun-Yu and Lee, Ying-Ying and Chen, Chi-Jen and Lin, Huey-Juan and Smith, Eric Edward and Yeh, Poh-Shiow and Hsin, Yue-Loong},
	booktitle = {BioMed research international},
	title = {Automatic Detection and Quantification of Acute Cerebral Infarct by Fuzzy Clustering and Histographic Characterization on Diffusion Weighted MR Imaging and Apparent Diffusion Coefficient Map},
	year = {2014}
}

@article{RAPID,
	abstract = {Abstract Diffusion?perfusion mismatch can be used to identify acute stroke patients that could benefit from reperfusion therapies. Early assessment of the mismatch facilitates necessary diagnosis and treatment decisions in acute stroke. We developed the RApid processing of PerfusIon and Diffusion (RAPID) for unsupervised, fully automated processing of perfusion and diffusion data for the purpose of expedited routine clinical assessment. The RAPID system computes quantitative perfusion maps (cerebral blood volume, CBV; cerebral blood flow, CBF; mean transit time, MTT; and the time until the residue function reaches its peak, Tmax) using deconvolution of tissue and arterial signals. Diffusion?weighted imaging/perfusion?weighted imaging (DWI/PWI) mismatch is automatically determined using infarct core segmentation of ADC maps and perfusion deficits segmented from Tmax maps. The performance of RAPID was evaluated on 63 acute stroke cases, in which diffusion and perfusion lesion volumes were outlined by both a human reader and the RAPID system. The correlation of outlined lesion volumes obtained from both methods was r2 = 0.99 for DWI and r2 = 0.96 for PWI. For mismatch identification, RAPID showed 100\% sensitivity and 91\% specificity. The mismatch information is made available on the hospital's PACS within 5?7 min. Results indicate that the automated system is sufficiently accurate and fast enough to be used for routine care as well as in clinical trials. J. Magn. Reson. Imaging 2010;32:1024?1037. ? 2010 Wiley?Liss, Inc.},
	author = {Matus, Straka and W., Albers Gregory and Roland, Bammer},
	doi = {10.1002/jmri.22338; 10.1002/jmri.22338},
	issn = {1053-1807},
	journal = {J. Magn. Reson. Imaging},
	keywords = {perfusion; diffusion; mismatch; acute stroke; automated data processing},
	note = {doi: 10.1002/jmri.22338},
	number = {5},
	pages = {1024--1037},
	publisher = {Wiley-Blackwell},
	risfield_0_t2 = {Journal of Magnetic Resonance Imaging},
	risfield_1_m3 = {doi: 10.1002/jmri.22338},
	title = {Real‐time diffusion‐perfusion mismatch analysis in acute stroke},
	volume = {32},
	year = {2010}
}

@book{ArterialTreesSegmentationReview,
	author = {{Lesage David} and {D. Angelini Elsa} and {Bloch Isabelle} and {Funka-Lea Gareth}},
	doi = {10.1016/j.media.2009.07.011},
	journal = {Medical Image Analysis},
	month = dec,
	pages = {819--845},
	title = {A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes},
	volume = {13},
	year = {2009}
}

@inproceedings{RBMIntro,
	address = {Berlin, Heidelberg},
	author = {Fischer, Asja and Igel, Christian},
	booktitle = {Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications},
	editor = {Alvarez, Luis and Mejail, Marta and Gomez, Luis and Jacobo, Julio},
	isbn = {978-3-642-33275-3},
	pages = {14--36},
	publisher = {Springer Berlin Heidelberg},
	title = {An Introduction to Restricted Boltzmann Machines},
	year = {2012}
}

@inproceedings{LSTMPorcine,
	author = {{Zhang}, D. and {Icke}, I. and {Dogdas}, B. and {Parimal}, S. and {Sampath}, S. and {Forbes}, J. and {Bagchi}, A. and {Chin}, C. and {Chen}, A.},
	booktitle = {2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)},
	doi = {10.1109/ISBI.2018.8363618},
	issn = {1945-8452},
	keywords = {biomedical MRI; cardiology; diseases; image segmentation; medical image processing; recurrent neural nets; multilevel convolutional LSTM model; infarcted porcine cine MR images; short-axis cine MR images; image inhomogeneity; LV myocardium segmentation task; deep convolutional neural networks; heart motion; recurrent neural network; short-term memory model; consecutive frames; surgically induced myocardial infarction; image features; left ventricle myocardium segmentation; multilevel ConvLSTM models; cardiac cine MR slices; one-level ConvLSTM approach; RNN; ResNet-56 CNN; leave-one-out experiment; Myocardium; Image segmentation; Training; Feature extraction; Heart; Image resolution; Magnetic resonance imaging; Cardiac cine MRI; convolutional neural network; recurrent neural network; LSTM},
	month = {April},
	number = {},
	pages = {470--473},
	title = {A multi-level convolutional LSTM model for the segmentation of left ventricle myocardium in infarcted porcine cine MR images},
	volume = {},
	year = {2018}
}

@article{PeepholeLSTM,
	acmid = {944925},
	author = {Gers, Felix A. and Schraudolph, Nicol N. and Schmidhuber, J{\"u}rgen},
	doi = {10.1162/153244303768966139},
	issn = {1532-4435},
	issue_date = {3/1/2003},
	journal = {J. Mach. Learn. Res.},
	keywords = {long short-term memory; recurrent neural networks; timing},
	month = mar,
	numpages = {29},
	pages = {115--143},
	publisher = {JMLR.org},
	title = {Learning Precise Timing with Lstm Recurrent Networks},
	url = {https://doi.org/10.1162/153244303768966139},
	volume = {3},
	year = {2003}
}

@inproceedings{Maxpool,
	author = {{Ranzato}, M. and {Huang}, F. J. and {Boureau}, Y. and {LeCun}, Y.},
	booktitle = {2007 IEEE Conference on Computer Vision and Pattern Recognition},
	doi = {10.1109/CVPR.2007.383157},
	issn = {1063-6919},
	keywords = {feature extraction; object recognition; unsupervised learning; unsupervised learning; invariant feature hierarchy; object recognition; feature extractor; multiple convolution filters; feature-pooling layer; Unsupervised learning; Object recognition; Feature extraction; Computer vision; Detectors; Computer architecture; Gabor filters; Object detection; Convolution; Supervised learning},
	month = {June},
	number = {},
	pages = {1--8},
	title = {Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition},
	volume = {},
	year = {2007}
}

@article{3dUnet,
	archiveprefix = {arXiv},
	author = {{\c C}i{\c c}ek, {\"O}zg{\"u}n and Abdulkadir, Ahmed and Lienkamp, Soeren S. and Brox, Thomas and Ronneberger, Olaf},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/bib/journals/corr/CicekALBR16},
	eprint = {1606.06650},
	journal = {CoRR},
	timestamp = {Mon, 13 Aug 2018 16:47:29 +0200},
	title = {3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation},
	url = {http://arxiv.org/abs/1606.06650},
	volume = {abs/1606.06650},
	year = {2016}
}

@misc{isles2022,
  doi = {10.48550/ARXIV.2206.06694},
  
  url = {https://arxiv.org/abs/2206.06694},
  
  author = {Petzsche, Moritz Roman Hernandez and de la Rosa, Ezequiel and Hanning, Uta and Wiest, Roland and Pinilla, Waldo Enrique Valenzuela and Reyes, Mauricio and Meyer, Maria Ines and Liew, Sook-Lei and Kofler, Florian and Ezhov, Ivan and Robben, David and Hutton, Alexander and Friedrich, Tassilo and Zarth, Teresa and Bürkle, Johannes and Baran, The Anh and Menze, Bjoern and Broocks, Gabriel and Meyer, Lukas and Zimmer, Claus and Boeckh-Behrens, Tobias and Berndt, Maria and Ikenberg, Benno and Wiestler, Benedikt and Kirschke, Jan S.},
  
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {Creative Commons Attribution Share Alike 4.0 International}
}

@article{sanet,
	doi = {10.1016/j.compbiomed.2023.106717},
  
	url = {https://doi.org/10.10162Fj.compbiomed.2023.106717},
  
	year = 2023,
	month = {apr},
  
	publisher = {Elsevier {BV}
},
  
	volume = {156},
  
	pages = {106717},
  
	author = {Weiyi Yu and Zhizhong Huang and Junping Zhang and Hongming Shan},
  
	title = {{SAN}-Net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization},
  
	journal = {Computers in Biology and Medicine}
}



@ARTICLE{msdfnet,

  author={Liu, Xinfeng and Yang, Hao and Qi, Kehan and Dong, Pei and Liu, Qiegen and Liu, Xin and Wang, Rongpin and Wang, Shanshan},

  journal={IEEE Access}, 

  title={MSDF-Net: Multi-Scale Deep Fusion Network for Stroke Lesion Segmentation}, 

  year={2019},

  volume={7},

  number={},

  pages={178486-178495},

  doi={10.1109/ACCESS.2019.2958384}
  }
  

@article{effi,
	doi = {10.1016/j.media.2016.10.004},
  
	url = {https://doi.org/10.10162Fj.media.2016.10.004},
  
	year = 2017,
	month = {feb},
  
	publisher = {Elsevier {BV}
},
  
	volume = {36},
  
	pages = {61--78},
  
	author = {Konstantinos Kamnitsas and Christian Ledig and Virginia F.J. Newcombe and Joanna P. Simpson and Andrew D. Kane and David K. Menon and Daniel Rueckert and Ben Glocker},
  
	title = {Efficient multi-scale 3D {CNN} with fully connected {CRF} for accurate brain lesion segmentation},
  
	journal = {Medical Image Analysis}
}
@article{gateatt,
  title={Delineation of ischemic lesion from brain MRI using attention gated fully convolutional network},
  author={Karthik, R and Radhakrishnan, Menaka and Rajalakshmi, R and Raymann, Joel},
  journal={Biomedical Engineering Letters},
  volume={11},
  pages={3--13},
  year={2021},
  publisher={Springer}
}
@article{multipath,
  title={A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images},
  author={Xue, Yunzhe and Farhat, Fadi G and Boukrina, Olga and Barrett, AM and Binder, Jeffrey R and Roshan, Usman W and Graves, William W},
  journal={NeuroImage: Clinical},
  volume={25},
  pages={102118},
  year={2020},
  publisher={Elsevier}
}

@ARTICLE{segnet,

  author={Badrinarayanan, Vijay and Kendall, Alex and Cipolla, Roberto},

  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 

  title={SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation}, 

  year={2017},

  volume={39},

  number={12},

  pages={2481-2495},

  doi={10.1109/TPAMI.2016.2644615}}

@article{resunet,
	doi = {10.1109/lgrs.2018.2802944},
  
	url = {https://doi.org/10.11092Flgrs.2018.2802944},
  
	year = 2018,
	month = {may},
  
	publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
  
	volume = {15},
  
	number = {5},
  
	pages = {749--753},
  
	author = {Zhengxin Zhang and Qingjie Liu and Yunhong Wang},
  
	title = {Road Extraction by Deep Residual U-Net},
  
	journal = {{IEEE} Geoscience and Remote Sensing Letters}
}

@misc{psp,
      title={Pyramid Scene Parsing Network}, 
      author={Hengshuang Zhao and Jianping Shi and Xiaojuan Qi and Xiaogang Wang and Jiaya Jia},
      year={2017},
      eprint={1612.01105},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@misc{deeplab,
      title={Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation}, 
      author={Liang-Chieh Chen and Yukun Zhu and George Papandreou and Florian Schroff and Hartwig Adam},
      year={2018},
      eprint={1802.02611},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@article{brainseg,
  title={Brain SegNet: 3D local refinement network for brain lesion segmentation},
  author={Hu, Xiaojun and Luo, Weijian and Hu, Jiliang and Guo, Sheng and Huang, Weilin and Scott, Matthew R and Wiest, Roland and Dahlweid, Michael and Reyes, Mauricio},
  journal={BMC medical imaging},
  volume={20},
  pages={1--10},
  year={2020},
  publisher={Springer}
}

@misc{nnUnet,
      title={nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation}, 
      author={Fabian Isensee and Jens Petersen and Andre Klein and David Zimmerer and Paul F. Jaeger and Simon Kohl and Jakob Wasserthal and Gregor Koehler and Tobias Norajitra and Sebastian Wirkert and Klaus H. Maier-Hein},
      year={2018},
      eprint={1809.10486},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@misc{UneTR,
      title={UNETR: Transformers for 3D Medical Image Segmentation}, 
      author={Ali Hatamizadeh and Yucheng Tang and Vishwesh Nath and Dong Yang and Andriy Myronenko and Bennett Landman and Holger Roth and Daguang Xu},
      year={2021},
      eprint={2103.10504},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}
@article{Factorizer,
	doi = {10.1016/j.media.2022.102706},
  
	url = {https://doi.org/10.1016%2Fj.media.2022.102706},
  
	year = 2023,
	month = {feb},
  
	publisher = {Elsevier {BV}
},
  
	volume = {84},
  
	pages = {102706},
  
	author = {Pooya Ashtari and Diana M. Sima and Lieven De Lathauwer and Dominique Sappey-Marinier and Frederik Maes and Sabine Van Huffel},
  
	title = {Factorizer: A scalable interpretable approach to context modeling for medical image segmentation},
  
	journal = {Medical Image Analysis}
}
@misc{segresnet,
      title={3D MRI brain tumor segmentation using autoencoder regularization}, 
      author={Andriy Myronenko},
      year={2018},
      eprint={1810.11654},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@article{reccnn,
  title={Convolutional recurrent neural networks for dynamic MR image reconstruction},
  author={Qin, Chen and Schlemper, Jo and Caballero, Jose and Price, Anthony N and Hajnal, Joseph V and Rueckert, Daniel},
  journal={IEEE transactions on medical imaging},
  volume={38},
  number={1},
  pages={280--290},
  year={2018},
  publisher={IEEE}
}
@article{mrirec,
  title={An End-to-End Recurrent Neural Network for Radial MR Image Reconstruction},
  author={Oh, Changheun and Chung, Jun-Young and Han, Yeji},
  journal={Sensors},
  volume={22},
  number={19},
  pages={7277},
  year={2022},
  publisher={MDPI}
}
@misc{resnet,
      title={Deep Residual Learning for Image Recognition}, 
      author={Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
      year={2015},
      eprint={1512.03385},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@misc{gru,
      title={On the Properties of Neural Machine Translation: Encoder-Decoder Approaches}, 
      author={Kyunghyun Cho and Bart van Merrienboer and Dzmitry Bahdanau and Yoshua Bengio},
      year={2014},
      eprint={1409.1259},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
@misc{segres,
      title={Automated ischemic stroke lesion segmentation from 3D MRI}, 
      author={Md Mahfuzur Rahman Siddique and Dong Yang and Yufan He and Daguang Xu and Andriy Myronenko},
      year={2022},
      eprint={2209.09546},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}
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}
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}
@misc{axial,
      title={Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation}, 
      author={Huiyu Wang and Yukun Zhu and Bradley Green and Hartwig Adam and Alan Yuille and Liang-Chieh Chen},
      year={2020},
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      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@Article{abbts,
AUTHOR = {Tian, Weiwei and Li, Dengwang and Lv, Mengyu and Huang, Pu},
TITLE = {Axial Attention Convolutional Neural Network for Brain Tumor Segmentation with Multi-Modality MRI Scans},
JOURNAL = {Brain Sciences},
VOLUME = {13},
YEAR = {2023},
NUMBER = {1},
ARTICLE-NUMBER = {12},
URL = {https://www.mdpi.com/2076-3425/13/1/12},
PubMedID = {36671994},
ISSN = {2076-3425},
ABSTRACT = {Accurately identifying tumors from MRI scans is of the utmost importance for clinical diagnostics and when making plans regarding brain tumor treatment. However, manual segmentation is a challenging and time-consuming process in practice and exhibits a high degree of variability between doctors. Therefore, an axial attention brain tumor segmentation network was established in this paper, automatically segmenting tumor subregions from multi-modality MRIs. The axial attention mechanism was employed to capture richer semantic information, which makes it easier for models to provide local&ndash;global contextual information by incorporating local and global feature representations while simplifying the computational complexity. The deep supervision mechanism is employed to avoid vanishing gradients and guide the AABTS-Net to generate better feature representations. The hybrid loss is employed in the model to handle the class imbalance of the dataset. Furthermore, we conduct comprehensive experiments on the BraTS 2019 and 2020 datasets. The proposed AABTS-Net shows greater robustness and accuracy, which signifies that the model can be employed in clinical practice and provides a new avenue for medical image segmentation systems.},
DOI = {10.3390/brainsci13010012}
}
@misc{attention,
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      year={2023},
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      archivePrefix={arXiv},
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}


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@Article{aabts,
AUTHOR = {Tian, Weiwei and Li, Dengwang and Lv, Mengyu and Huang, Pu},
TITLE = {Axial Attention Convolutional Neural Network for Brain Tumor Segmentation with Multi-Modality MRI Scans},
JOURNAL = {Brain Sciences},
VOLUME = {13},
YEAR = {2023},
NUMBER = {1},
ARTICLE-NUMBER = {12},
URL = {https://www.mdpi.com/2076-3425/13/1/12},
PubMedID = {36671994},
ISSN = {2076-3425},
ABSTRACT = {Accurately identifying tumors from MRI scans is of the utmost importance for clinical diagnostics and when making plans regarding brain tumor treatment. However, manual segmentation is a challenging and time-consuming process in practice and exhibits a high degree of variability between doctors. Therefore, an axial attention brain tumor segmentation network was established in this paper, automatically segmenting tumor subregions from multi-modality MRIs. The axial attention mechanism was employed to capture richer semantic information, which makes it easier for models to provide local&ndash;global contextual information by incorporating local and global feature representations while simplifying the computational complexity. The deep supervision mechanism is employed to avoid vanishing gradients and guide the AABTS-Net to generate better feature representations. The hybrid loss is employed in the model to handle the class imbalance of the dataset. Furthermore, we conduct comprehensive experiments on the BraTS 2019 and 2020 datasets. The proposed AABTS-Net shows greater robustness and accuracy, which signifies that the model can be employed in clinical practice and provides a new avenue for medical image segmentation systems.},
DOI = {10.3390/brainsci13010012}
}
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	doi = {10.1007/978-3-030-32248-9_28},
  
	url = {https://doi.org/10.1007%2F978-3-030-32248-9_28},
  
	year = 2019,
	publisher = {Springer International Publishing},
  
	pages = {247--255},
  
	author = {Kehan Qi and Hao Yang and Cheng Li and Zaiyi Liu and Meiyun Wang and Qiegen Liu and Shanshan Wang},
  
	title = {X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-Range Dependencies},
  
	booktitle = {Lecture Notes in Computer Science}
}


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	doi = {10.1007/s42979-021-00835-x},
  
	url = {https://doi.org/10.1007%2Fs42979-021-00835-x},
  
	year = 2021,
	month = {aug},
  
	publisher = {Springer Science and Business Media {LLC}
},
  
	volume = {2},
  
	number = {6},
  
	author = {Hritam Basak and Rukhshanda Hussain and Ajay Rana},
  
	title = {{DFENet}: A Novel Dimension Fusion Edge Guided Network for Brain {MRI} Segmentation},
  
	journal = {{SN} Computer Science}
}
@misc{unetm,
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      year={2018},
      eprint={1807.10165},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@misc{vit,
      title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale}, 
      author={Alexey Dosovitskiy and Lucas Beyer and Alexander Kolesnikov and Dirk Weissenborn and Xiaohua Zhai and Thomas Unterthiner and Mostafa Dehghani and Matthias Minderer and Georg Heigold and Sylvain Gelly and Jakob Uszkoreit and Neil Houlsby},
      year={2021},
      eprint={2010.11929},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
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      title={OctopusNet: A Deep Learning Segmentation Network for Multi-modal Medical Images}, 
      author={Yu Chen and Jiawei Chen and Dong Wei and Yuexiang Li and Yefeng Zheng},
      year={2019},
      eprint={1906.02031},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}
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  title={Adaptive feature recombination and recalibration for semantic segmentation with fully convolutional networks},
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}
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  title={Boundary-aware fully convolutional network for brain tumor segmentation},
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}
@inproceedings{early3,
  title={Volume-based analysis of 6-month-old infant brain MRI for autism biomarker identification and early diagnosis},
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  pages={411--419},
  year={2018},
  organization={Springer}
}
@inproceedings{late2,
  title={Registration-free infant cortical surface parcellation using deep convolutional neural networks},
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@inproceedings{late1,
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  publisher={Elsevier}
}
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  title={Acute ischemic stroke lesion core segmentation in CT perfusion images using fully convolutional neural networks},
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  publisher={Elsevier}
}
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}

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  title={Fine-grained brain tissue segmentation for brain modeling of stroke patient},
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  publisher={Elsevier}
}

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  pages={103044},
  year={2022},
  publisher={Elsevier}
}

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}
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}
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  year={2020},
  publisher={Elsevier}
}
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@INPROCEEDINGS{jon,

  author={Kobold, J. and Vigneron, V. and Maaref, H. and Fourer, D. and Aghasaryan, M. and Alecu, C. and Chausson, N. and L’Hermitte, Y. and Smadja, D. and Lang, E. and Tome, A. M.},

  booktitle={2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA)}, 

  title={Stroke Thrombus Segmentation on SWAN with Multi-Directional U-Nets}, 

  year={2019},

  volume={},

  number={},

  pages={1-6},

  doi={10.1109/IPTA.2019.8936074}}

@Article{polarunet,
AUTHOR = {Zoetmulder, Riaan and Bruggeman, Agnetha A. E. and Išgum, Ivana and Gavves, Efstratios and Majoie, Charles B. L. M. and Beenen, Ludo F. M. and Dippel, Diederik W. J. and Boodt, Nikkie and den Hartog, Sanne J. and van Doormaal, Pieter J. and Cornelissen, Sandra A. P. and Roos, Yvo B. W. E. M. and Brouwer, Josje and Schonewille, Wouter J. and Pirson, Anne F. V. and van Zwam, Wim H. and van der Leij, Christiaan and Brans, Rutger J. B. and van Es, Adriaan C. G. M. and Marquering, Henk A.},
TITLE = {Deep-Learning-Based Thrombus Localization and Segmentation in Patients with Posterior Circulation Stroke},
JOURNAL = {Diagnostics},
VOLUME = {12},
YEAR = {2022},
NUMBER = {6},
ARTICLE-NUMBER = {1400},
URL = {https://www.mdpi.com/2075-4418/12/6/1400},
PubMedID = {35741209},
ISSN = {2075-4418},
ABSTRACT = {Thrombus volume in posterior circulation stroke (PCS) has been associated with outcome, through recanalization. Manual thrombus segmentation is impractical for large scale analysis of image characteristics. Hence, in this study we develop the first automatic method for thrombus localization and segmentation on CT in patients with PCS. In this multi-center retrospective study, 187 patients with PCS from the MR CLEAN Registry were included. We developed a convolutional neural network (CNN) that segments thrombi and restricts the volume-of-interest (VOI) to the brainstem (Polar-UNet). Furthermore, we reduced false positive localization by removing small-volume objects, referred to as volume-based removal (VBR). Polar-UNet is benchmarked against a CNN that does not restrict the VOI (BL-UNet). Performance metrics included the intra-class correlation coefficient (ICC) between automated and manually segmented thrombus volumes, the thrombus localization precision and recall, and the Dice coefficient. The majority of the thrombi were localized. Without VBR, Polar-UNet achieved a thrombus localization recall of 0.82, versus 0.78 achieved by BL-UNet. This high recall was accompanied by a low precision of 0.14 and 0.09. VBR improved precision to 0.65 and 0.56 for Polar-UNet and BL-UNet, respectively, with a small reduction in recall to 0.75 and 0.69. The Dice coefficient achieved by Polar-UNet was 0.44, versus 0.38 achieved by BL-UNet with VBR. Both methods achieved ICCs of 0.41 (95% CI: 0.27–0.54). Restricting the VOI to the brainstem improved the thrombus localization precision, recall, and segmentation overlap compared to the benchmark. VBR improved thrombus localization precision but lowered recall.},
DOI = {10.3390/diagnostics12061400}
}
@phdthesis{thesis,
  TITLE = {{Deep Learning for lesion and thrombus segmentation from cerebral MRI}},
  AUTHOR = {Kobold, Jonathan},
  URL = {https://theses.hal.science/tel-03592570},
  NUMBER = {2019SACLE044},
  SCHOOL = {{Universit{\'e} Paris Saclay (COmUE)}},
  YEAR = {2019},
  MONTH = Dec,
  KEYWORDS = {AVC isch{\'e}mique ; Automatic segmentation ; Lstm ; Imbalanced data ; AVC isch{\'e}mique ; Segmentation automatique ; Lstm ; Donn{\'e}es d{\'e}s{\'e}quilibr{\'e}es},
  TYPE = {Theses},
  PDF = {https://theses.hal.science/tel-03592570/file/2019SACLE044.pdf},
  HAL_ID = {tel-03592570},
  HAL_VERSION = {v1},
}

@Article{hemisphere,
AUTHOR = {Tolhuisen, Manon L. and Ponomareva, Elena and Boers, Anne M. M. and Jansen, Ivo G. H. and Koopman, Miou S. and Sales Barros, Renan and Berkhemer, Olvert A. and van Zwam, Wim H. and van der Lugt, Aad and Majoie, Charles B. L. M. and Marquering, Henk A.},
TITLE = {A Convolutional Neural Network for Anterior Intra-Arterial Thrombus Detection and Segmentation on Non-Contrast Computed Tomography of Patients with Acute Ischemic Stroke},
JOURNAL = {Applied Sciences},
VOLUME = {10},
YEAR = {2020},
NUMBER = {14},
ARTICLE-NUMBER = {4861},
URL = {https://www.mdpi.com/2076-3417/10/14/4861},
ISSN = {2076-3417},
ABSTRACT = {The aim of this study was to develop a convolutional neural network (CNN) that automatically detects and segments intra-arterial thrombi on baseline non-contrast computed tomography (NCCT) scans. We retrospectively collected computed tomography (CT)-scans of patients with an anterior circulation large vessel occlusion (LVO) from the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands trial, both for training (n = 86) and validation (n = 43). For testing we included patients with (n = 58) and without (n = 45) an LVO from our comprehensive stroke center. Ground truth was established by consensus between two experts using both CT angiography and NCCT. We evaluated the CNN for correct identification of a thrombus, its location and thrombus segmentation and compared these with the results of a neurologist in training and expert neuroradiologist. Sensitivity of the CNN thrombus detection was 0.86, vs. 0.95 and 0.79 for the neuroradiologists. Specificity was 0.65 for the network vs. 0.58 and 0.82 for the neuroradiologists. The CNN correctly identified the location of the thrombus in 79% of the cases, compared to 81% and 77% for the neuroradiologists. The sensitivity and specificity for thrombus identification and the rate for correct thrombus location assessment by the CNN were similar to those of expert neuroradiologists.},
DOI = {10.3390/app10144861}
}



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}