@article{Liu_arxiv2019,
author = {Liu, Yalong and Li, Jie and Wang, Miaomiao and Jiao, Zhicheng and Yang, Jian and Li, Xianjun},
year = {2019},
month = {10},
pages = {},
journal = {Arxiv},
title = {Trident Segmentation CNN: A Spatiotemporal Transformation CNN for Punctate White Matter Lesions Segmentation in Preterm Neonates}
}

@article{Liu_MICCAI2019,
author = {Liu, Yalong and Li, Jie and Wang, Ying and Wang, Miaomiao and Li, Xianjun and Jiao, Zhicheng and Yang, Jian and Gao, Xinbo},
year = {2019},
month = {10},
pages = {193-201},
journal = {MICCAI},
title = {Refined Segmentation R-CNN: A Two-Stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants},
isbn = {978-3-030-32247-2},
doi = {10.1007/978-3-030-32248-9_22}
}



@article{Oktay,
author = {Oktay, Ozan and Schlemper, Jo and Folgoc, Loic and Lee, Matthew and Heinrich, Mattias and Misawa, Kazunari and Mori, Kensaku and McDonagh, Steven and Hammerla, Nils and Kainz, Bernhard and Glocker, Ben and Rueckert, Daniel},
journal = {MIDL},
year = {2018},
month = {04},

pages = {},
title = {Attention U-Net: Learning Where to Look for the Pancreas}
}

@article{Guo,
author = {Guo, Ting and Duerden, Emma and Adams, Elysia and Chau, Vann and Branson, Helen and Chakravarty, M. and Poskitt, Kenneth and Synnes, Anne and Grunau, Ruth and Miller, Steven},
year = {2017},
month = {01},
pages = {10.1212/WNL.0000000000003606},
title = {Quantitative assessment of white matter injury in preterm neonates: Association with outcomes},
volume = {88},
journal = {Neurology},
doi = {10.1212/WNL.0000000000003606}
}

@InProceedings{Ronneberger,
author="Ronneberger, Olaf
and Fischer, Philipp
and Brox, Thomas",
editor="Navab, Nassir
and Hornegger, Joachim
and Wells, William M.
and Frangi, Alejandro F.",
title="U-Net: Convolutional Networks for Biomedical Image Segmentation",
booktitle="Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015",
year="2015",
publisher="Springer International Publishing",
address="Cham",
pages="234--241",
abstract="There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.",
isbn="978-3-319-24574-4"
}




@article{mukherjee, title={A fast segmentation-free fully automated approach to white matter injury detection in preterm infants}, volume={57}, DOI={10.1007/s11517-018-1829-9}, number={1}, journal={Medical and Biological Engineering and Computing}, author={Mukherjee, Subhayan and Cheng, Irene and Miller, Steven and Guo, Ting and Chau, Vann and Basu, Anup}, year={2019}, month={Jan}, pages={71–87}}


@INPROCEEDINGS{Martin,
author={M. {Martin} and B. {Sciolla} and M. {Sdika} and X. {Wang} and P. {Quetin} and P. {Delachartre}},
booktitle={2018 IEEE International Ultrasonics Symposium (IUS)},
title={Automatic Segmentation of the Cerebral Ventricle in Neonates Using Deep Learning with 3D Reconstructed Freehand Ultrasound Imaging},
year={2018},
volume={},
number={},
pages={1-4},
keywords={biomedical ultrasonics;brain;image reconstruction;image segmentation;learning (artificial intelligence);medical image processing;preterm neonates;ventricu-lomegaly;cerebral ventricle system;brain damage;clinical practice;2D coronal hand-held ultrasonographic scans;CVS dilation;CVS volumes;3D reconstruction method;automatic deep learning segmentation algorithm;MAD;manual segmentation;3D reconstructed volume;mean value;Hausdorff distance;computation time;automatic segmentation;freehand ultrasound;mean absolute distance;Three-dimensional displays;Image segmentation;Image reconstruction;Two dimensional displays;Pediatrics;Training;Manuals;preterm neonate;3D reconstruction;ventricu-lomegaly;segmentation;deep learning},
doi={10.1109/ULTSYM.2018.8580214},
ISSN={1948-5719},
month={Oct},}

@article{nguyen_brains_2019,
	title = {The brain’s kryptonite: {Overview} of punctate white matter lesions in neonates},
	volume = {77},
	issn = {07365748},
	shorttitle = {The brain’s kryptonite},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0736574818302752},
	doi = {10.1016/j.ijdevneu.2019.04.006},
	abstract = {With increasing advances in the field of medical brain imaging, the known spectrum of white matter lesions has expanded, and we can now assess the presence of punctate white matter lesions (PWML). These focal small lesions are quite frequently detected in the preterm infant and in full-term infants with congenital heart malformations with, some studies reporting a link between these lesions and adverse long-term outcomes. The etiology of PWML has sparked a lot of questions over the years, some of which still remain unanswered. This narrative review will bring an overview of current knowledge and their significant clinical importance in the newborn brain.},
	language = {en},
	urldate = {2019-11-16},
	journal = {International Journal of Developmental Neuroscience},
	author = {Nguyen, Annie L.A. and Ding, Yang and Suffren, Sabrina and Londono, Irène and Luck, David and Lodygensky, Gregory A.},
	month = oct,
	year = {2019},
	pages = {77--88}
}



@article{pierrat_neurodevelopmental_2017,
	title = {Neurodevelopmental outcome at 2 years for preterm children born at 22 to 34 weeks’ gestation in {France} in 2011: {EPIPAGE}-2 cohort study},
	issn = {0959-8138, 1756-1833},
	shorttitle = {Neurodevelopmental outcome at 2 years for preterm children born at 22 to 34 weeks’ gestation in {France} in 2011},
	url = {http://www.bmj.com/lookup/doi/10.1136/bmj.j3448},
	doi = {10.1136/bmj.j3448},
	abstract = {OBJECTIVES To describe neurodevelopmental outcomes at 2 years corrected age for children born alive at 22-26, 27-31, and 32-34 weeks’ gestation in 2011, and to evaluate changes since 1997.},
	language = {en},
	urldate = {2019-11-16},
	journal = {BMJ},
	author = {Pierrat, Véronique and Marchand-Martin, Laetitia and Arnaud, Catherine and Kaminski, Monique and Resche-Rigon, Matthieu and Lebeaux, Cécile and Bodeau-Livinec, Florence and Morgan, Andrei S and Goffinet, François and Marret, Stéphane and Ancel, Pierre-Yves and {and the EPIPAGE-2 writing group}},
	month = aug,
	year = {2017},
	pages = {j3448}
}

@Article{Tusor2017,
author={Tusor, Nora
and Benders, Manon J.
and Counsell, Serena J.
and Nongena, Phumza
and Ederies, Moegamad A.
and Falconer, Shona
and Chew, Andrew
and Gonzalez-Cinca, Nuria
and Hajnal, Joseph V.
and Gangadharan, Sunay
and Chatzi, Vasiliki
and Kersbergen, Karina J.
and Kennea, Nigel
and Azzopardi, Denis V.
and Edwards, A. David},
title={Punctate White Matter Lesions Associated With Altered Brain Development And Adverse Motor Outcome In Preterm Infants},
journal={Scientific Reports},
year={2017},
volume={7},
number={1},
pages={13250},
abstract={Preterm infants who develop neurodevelopmental impairment do not always have recognized abnormalities on cerebral ultrasound, a modality routinely used to assess prognosis. In a high proportion of infants, MRI detects punctate white matter lesions that are not seen on ultrasonography. To determine the relation of punctate lesions to brain development and early neurodevelopmental outcome we used multimodal brain MRI to study a large cohort of preterm infants. Punctate lesions without other focal cerebral or cerebellar lesions were detected at term equivalent age in 123 (24.3\%) (59 male) of the 506 infants, predominantly in the centrum semiovale and corona radiata. Infants with lesions had higher gestational age, birth weight, and less chronic lung disease. Punctate lesions showed a dose dependent relation to abnormalities in white matter microstructure, assessed with tract-based spatial statistics, and reduced thalamic volume (p < 0.0001), and predicted unfavourable motor outcome at a median (range) corrected age of 20.2 (18.4-26.3) months with sensitivity (95\% confidence intervals) 71 (43-88) and specificity 72 (69-77). Punctate white matter lesions without associated cerebral lesions are common in preterm infants currently not regarded as at highest risk for cerebral injury, and are associated with widespread neuroanatomical abnormalities and adverse early neurodevelopmental outcome.},
issn={2045-2322},
doi={10.1038/s41598-017-13753-x},
url={https://doi.org/10.1038/s41598-017-13753-x}
}

@article{lin_goyal_girshick_he_dollar_2017, title={Focal Loss for Dense Object Detection}, DOI={10.1109/iccv.2017.324}, journal={2017 IEEE International Conference on Computer Vision (ICCV)}, author={Lin, Tsung-Yi and Goyal, Priya and Girshick, Ross and He, Kaiming and Dollar, Piotr}, year={2017}}