@article{Dewan.2018,
 author = {Dewan, M. C. and Rattani, A. and Fieggen, G. and Arraez, M. A. and Servadei, F. and Boop, F. A. and Johnson, W. D. and Warf, B. C. and Park, K. B.},
 year = {2018},
 title = {Global neurosurgery: the current capacity and deficit in the provision of essential neurosurgical care. Executive Summary of the Global Neurosurgery Initiative at the Program in Global Surgery and Social Change},
 pages = {1--10},
 journal = {Journal of neurosurgery},
 doi = {10.3171/2017.11.JNS171500}}
}
@InProceedings{Kalavakonda_2019_CVPR_Workshops,
author = {Kalavakonda, N. and Hannaford, B. and Qazi, Z. and Sekhar, L.},
title = {Autonomous Neurosurgical Instrument Segmentation Using End-To-End Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}

@INPROCEEDINGS{Sun.2018,
  author={Sun, D. and Yang, X. and Liu, M. and Kautz, J.},
  booktitle={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition}, 
  title={PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume}, 
  year={2018},
  volume={},
  number={},
  pages={8934-8943},
  abstract={We present a compact but effective CNN model for optical flow, called PWC-Net. PWC-Net has been designed according to simple and well-established principles: pyramidal processing, warping, and the use of a cost volume. Cast in a learnable feature pyramid, PWC-Net uses the current optical flow estimate to warp the CNN features of the second image. It then uses the warped features and features of the first image to construct a cost volume, which is processed by a CNN to estimate the optical flow. PWC-Net is 17 times smaller in size and easier to train than the recent FlowNet2 model. Moreover, it outperforms all published optical flow methods on the MPI Sintel final pass and KITTI 2015 benchmarks, running at about 35 fps on Sintel resolution (1024 Ã × 436) images. Our models are available on our project website.},
  keywords={convolutional neural nets;feature extraction;image sequences;learning (artificial intelligence);warping;cost volume;pyramidal processing;learnable feature pyramid;current optical flow estimate;CNN features;warped features;published optical flow methods;CNN model;PWC-Net;FlowNet2 model;Optical imaging;Adaptive optics;Benchmark testing;Computational modeling;Task analysis;Feature extraction;Computer vision},
  doi={10.1109/CVPR.2018.00931},
  ISSN={2575-7075},
  month={June},}

@ARTICLE{Bylinskii.2019,
  author={Bylinskii, Z. and Judd, T. and Oliva, A. and Torralba, A. and Durand, F.},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={What Do Different Evaluation Metrics Tell Us About Saliency Models?}, 
  year={2019},
  volume={41},
  number={3},
  pages={740-757},
  abstract={How best to evaluate a saliency model's ability to predict where humans look in images is an open research question. The choice of evaluation metric depends on how saliency is defined and how the ground truth is represented. Metrics differ in how they rank saliency models, and this results from how false positives and false negatives are treated, whether viewing biases are accounted for, whether spatial deviations are factored in, and how the saliency maps are pre-processed. In this paper, we provide an analysis of 8 different evaluation metrics and their properties. With the help of systematic experiments and visualizations of metric computations, we add interpretability to saliency scores and more transparency to the evaluation of saliency models. Building off the differences in metric properties and behaviors, we make recommendations for metric selections under specific assumptions and for specific applications.},
  keywords={image processing;evaluation metric;saliency models;saliency maps;metric computations;saliency scores;metric properties;metric selections;Measurement;Computational modeling;Analytical models;Visualization;Benchmark testing;Observers;Task analysis;Saliency models;evaluation metrics;benchmarks;fixation maps;saliency applications},
  doi={10.1109/TPAMI.2018.2815601},
  ISSN={1939-3539},
  month={March},}

@INPROCEEDINGS{Huang.2017,
  author={Huang, G. and Liu, Z. and {Van Der Maaten}, L. and {Weinberger}, K. Q.},
  booktitle={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={Densely Connected Convolutional Networks}, 
  year={2017},
  volume={},
  number={},
  pages={2261-2269},
  abstract={Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections-one between each layer and its subsequent layer-our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet.},
  keywords={convolution;feedforward neural nets;learning (artificial intelligence);DenseNet;traditional convolutional networks;feature propagation;feature reuse;object recognition benchmark tasks;dense convolutional network;vanishing-gradient problem;Training;Convolution;Network architecture;Convolutional codes;Neural networks;Road transportation},
  doi={10.1109/CVPR.2017.243},
  ISSN={1063-6919},
  month={July},}

@article{TobiasRo.2020,
 author = {Ross, T. and Reinke, A. and Full, P. M. and Wagner, M. and Kenngott, H. and Apitz, M. and Hempe, H. and Mindroc Filimon, D. and Scholz, P. and Tran, T. N. and Bruno, P. and Arbeláez, P. and Bian, G.-B. and Bodenstedt, S. and Bolmgren, J. L. and Bravo-Sánchez, L. and Chen, H.-B. and González, C. and Guo, D. and Halvorsen, P. and Heng, P.-A. and Hosgor, E. and Hou, Z.-G. and Isensee, F. and Jha, D. and Jiang, T. and Jin, Y. and Kirtac, K. and Kletz, S. and Leger, S. and Li, Z. and Maier-Hein, K. H. and Ni, Z.-L. and Riegler, M. A. and Schoeffmann, K. and Shi, R. and Speidel, S. and Stenzel, M. and Twick, I. and Wang, G. and Wang, J. and Wang, L. and Wang, L. and Zhang, Y. and Zhou, Y.-J. and Zhu, L. and Wiesenfarth, M. and Kopp-Schneider, A. and Müller-Stich, B. P. and Maier-Hein, L.},
 year = {2020},
 title = {Comparative validation of multi-instance instrument segmentation in endoscopy: results of the ROBUST-MIS 2019 challenge},
 keywords = {generalization;minimally invasive surgery;Multi-instance instrument;robustness;surgical data science},
 pages = {101920},
 issn = {1361-8415},
 journal = {Medical Image Analysis},
 doi = {10.1016/j.media.2020.101920}
}

@article{Zhao.2019,
 author = {Zhao, Z. and Cai, T. and Chang, F. and Cheng, X.},
 year = {2019},
 title = {Real-time surgical instrument detection in robot-assisted surgery using a convolutional neural network cascade},
 keywords = {ATLAS Dione dataset;authors;bounding-box regression;cascading convolutional neural network;CNN;convolutional neural nets;convolutional neural network cascade;deep learning methods;detection heatmaps;EndoVis Challenge dataset;frame-by-frame detection method;hourglass network;image colour analysis;learning (artificial intelligence);mainstream detection methods;medical image processing;medical robotics;modified VGG network;object detection;real-time multitool detection;real-time multi-tool detection;real-time surgical instrument detection;regression analysis;RGB image frames;robot vision;robot-assisted surgery videos;single-tool detection;surgery;tool tip areas;vision component},
 pages = {275--279},
 volume = {6},
 number = {6},
 issn = {2053-3713},
 journal = {Healthcare technology letters},
 doi = {10.1049/htl.2019.0064},
 file = {http://www.ncbi.nlm.nih.gov/pubmed/32038871},
 file = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952255},
 file = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952255/}
}


@inproceedings{Li.2014,
 abstract = {Robust visual tracking of instruments is an important task in retinal microsurgery. In this context, the instruments are subject to a large variety of appearance changes due to illumination and other changes during a procedure, which makes the task very challenging. Most existing methods require collecting a sufficient amount of labelled data and yet perform poorly in handling appearance changes that are unseen in training data. To address these problems, we propose a new approach for robust instrument tracking. Specifically, we adopt an online learning technique that collects appearance samples of instruments on the fly and gradually learns a target-specific detector. Online learning enables the detector to reinforce its model and become more robust over time. The performance of the proposed method has been evaluated on a fully annotated dataset of retinal instruments in in-vivo retinal microsurgery and on a laparoscopy image sequence. In all experimental results, our proposed tracking approach shows superior performance compared to several other state-of-the-art approaches.},
 author = {Yeqing, L. and Chen, C. and Xiaolei, H. and Junzhou, H.},
 title = {Instrument Tracking via Online Learning in Retinal Microsurgery},
 pages = {464--471},
 publisher = {{Springer International Publishing}},
 isbn = {978-3-319-10404-1},
 editor = {Golland, Polina and Hata, Nobuhiko and Barillot, Christian and Hornegger, Joachim and Howe, Robert},
 booktitle = {Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2014},
 year = {2014},
 address = {Cham}
}

@inproceedings{Eivazi.2012,
author = {Eivazi, S. and Bednarik, R. and Tukiainen, M. and {von und zu Fraunberg}, M. and Leinonen, V. and J\"{a}\"{a}skel\"{a}inen, J. E.},
title = {Gaze Behaviour of Expert and Novice Microneurosurgeons Differs during Observations of Tumor Removal Recordings},
year = {2012},
isbn = {9781450312219},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
doi = {10.1145/2168556.2168641},
booktitle = {Proceedings of the Symposium on Eye Tracking Research and Applications},
pages = {377-–380},
numpages = {4},
keywords = {expertise, gaze tracking, microneurosurgery},
location = {Santa Barbara, California},
series = {ETRA '12}
}



@ARTICLE{Bouget.2015,
  author={{Bouget}, D. and {Benenson}, R. and {Omran}, M. and {Riffaud}, L. and {Schiele}, B. and {Jannin}, P.},
  journal={IEEE Transactions on Medical Imaging}, 
  title={Detecting Surgical Tools by Modelling Local Appearance and Global Shape}, 
  year={2015},
  volume={34},
  number={12},
  pages={2603-2617},
  doi={10.1109/TMI.2015.2450831}}
  
 @article{NicolaRieke.2016,
 author = {{Rieke}, N. and {Tan}, D. J. and {Chiara Amat di San Filippo}, N. and {Tombari}, F. and {Alsheakhali}. M. and {Belagiannis}, V. and {Eslami}, A. and {Navab}, N.},
 year = {2016},
 title = {Real-time localization of articulated surgical instruments in retinal microsurgery},
 keywords = {Pose estimation;Retinal microsurgery;Visual tracking},
 pages = {82--100},
 volume = {34},
 issn = {1361-8415},
 journal = {Medical Image Analysis},
 doi = {10.1016/j.media.2016.05.003}
}

@InProceedings{Mobarakol.2019,
author="Islam, M.
and Li, Y.
and Ren, H.",
editor="Shen, D.
and Liu, T.
and Peters, T. M.
and Staib, L. H.
and Essert, C.
and Zhou, S.
and Yap, P.-T.
and Khan, A.",
title="Learning Where to Look While Tracking Instruments in Robot-Assisted Surgery",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2019",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="412--420"}

@ARTICLE{Bak.2018,
  author={{Bak}, C. and {Kocak}, A. and {Erdem}, E. and {Erdem}, A.},
  journal={IEEE Transactions on Multimedia}, 
  title={Spatio-Temporal Saliency Networks for Dynamic Saliency Prediction}, 
  year={2018},
  volume={20},
  number={7},
  pages={1688-1698},
  doi={10.1109/TMM.2017.2777665}}