Multi-task recurrent convolutional network with correlation loss for surgical video analysis

Medical Image Analysis - Tập 59 - Trang 101572 - 2020
Yueming Jin1, Huaxia Li1, Qi Dou1, Hao Chen1, Jing Qin2, Chi-Wing Fu1, Pheng-Ann Heng1,3
1Department of Computer Science and Engineering, The Chinese University of Hong Kong, China
2Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, China
3T Stone Robotics Institute, The Chinese University of Hong Kong, China

Tài liệu tham khảo

Ahmidi, 2017, A dataset and benchmarks for segmentation and recognition of gestures in robotic surgery, IEEE Trans. Biomed. Eng., 64, 2025, 10.1109/TBME.2016.2647680 Al Hajj, 2017, Surgical tool detection in cataract surgery videos through multi-image fusion inside a convolutional neural network, 2002 Augenstein, I., Ruder, S., Søgaard, A., 2018. Multi-task learning of pairwise sequence classification tasks over disparate label spaces. In: arXiv preprint. UCL: 1802.09913. Bachman, 2014, Learning with pseudo-ensembles, 3365 Bhatia, 2007, Real-time identification of operating room state from video, 2, 1761 Blum, 2010, Modeling and segmentation of surgical workflow from laparoscopic video, 400 Bouget, 2017, Vision-based and marker-less surgical tool detection and tracking: a review of the literature, Med. Image Anal., 35, 633, 10.1016/j.media.2016.09.003 Bouget, 2015, Detecting surgical tools by modelling local appearance and global shape, IEEE Trans. Med. Imaging, 34, 2603, 10.1109/TMI.2015.2450831 Bragman, 2018, Uncertainty in multitask learning: Joint representations for probabilistic MR-only radiotherapy planning, 3 Bricon-Souf, 2007, Context awareness in health care: a review, Int. J. Med. Inf., 76, 2, 10.1016/j.ijmedinf.2006.01.003 Cadene, R., Robert, T., Thome, N., Cord, M., 2016. M2CAI workflow challenge: convolutional neural networks with time smoothing and hidden Markov model for video frames classification. In: arXiv preprint. UCL: 1610.05541. Choi, 2017, Surgical-tools detection based on convolutional neural network in laparoscopic robot-assisted surgery, 1756 Cleary, 2005, OR 2020: The operating room of the future, J. Laparosc. Adv. Surg. Techn. Part A, 15, 495, 10.1089/lap.2005.15.495 Dergachyova, 2016, Automatic data-driven real-time segmentation and recognition of surgical workflow, Int. J. Comput. Ass. Radiol. Surg., 1 DiPietro, 2016, Recognizing surgical activities with recurrent neural networks, 551 Donahue, 2015, Long-term recurrent convolutional networks for visual recognition and description, 2625 Dou, 2017, Automated pulmonary nodule detection via 3D ConvNets with online sample filtering and hybrid-loss residual learning, 630 Forestier, 2013, Multi-site study of surgical practice in neurosurgery based on surgical process models, J. Biomed. Inf., 46, 822, 10.1016/j.jbi.2013.06.006 Forestier, 2015, Automatic phase prediction from low-level surgical activities, Int. J. Comput. Ass. Radiol. Surgery, 10, 833, 10.1007/s11548-015-1195-0 Gebru, 2017, Fine-grained recognition in the wild: A multi-task domain adaptation approach, 1358 He, 2016, Deep residual learning for image recognition, 770 Hinami, 2017, Joint detection and recounting of abnormal events by learning deep generic knowledge, 3619 James, 2007, Eye-gaze driven surgical workflow segmentation, 110 Jin, Y., Cheng, K., Dou, Q., Heng, P.-A., 2019. Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video. In: arXiv preprint. UCL: 1907.07899. Jin, 2018, SV-RCNet: Workflow recognition from surgical videos using recurrent convolutional network, IEEE Trans. Med. Imaging, 37, 1114, 10.1109/TMI.2017.2787657 Klank, 2008, Automatic feature generation in endoscopic images, Int. J. Comput. Ass. Radiol. Surg., 3, 331, 10.1007/s11548-008-0223-8 Laina, 2017, Concurrent segmentation and localization for tracking of surgical instruments, 664 Lalys, 2013, Automatic knowledge-based recognition of low-level tasks in ophthalmological procedures, Int. J. Comput. Ass. Radiol. Surg., 8, 39, 10.1007/s11548-012-0685-6 Lalys, 2014, Surgical process modelling: a review, Int. J. Comput. Ass. Radiol. Surg., 9, 495, 10.1007/s11548-013-0940-5 Lalys, 2012, A framework for the recognition of high-level surgical tasks from video images for cataract surgeries, IEEE Trans. Biomed. Eng., 59, 966, 10.1109/TBME.2011.2181168 Lea, 2016, Surgical phase recognition: from instrumented ORs to hospitals around the world, 45 Liu, 2017, Hierarchical clustering multi-task learning for joint human action grouping and recognition, IEEE Trans. Pattern Anal. Mach. Intell., 39, 102, 10.1109/TPAMI.2016.2537337 Liu, 2018, Deep reinforcement learning for surgical gesture segmentation and classification, 247 Luo, H., Hu, Q., Jia, F., 2016. Surgical tool detection via multiple convolutional neural networks. http://camma.u-strasbg.fr/m2cai2016/reports/Luo-Tool.pdf. Mahmud, 2017, Joint prediction of activity labels and starting times in untrimmed videos, 5773 Nakawala, 2019, Âǣdeep-ontoâǥ network for surgical workflow and context recognition, Int. J. Comput. Ass. Radiol. Surg., 14, 685, 10.1007/s11548-018-1882-8 Padoy, 2012, Statistical modeling and recognition of surgical workflow, Med. Image Anal., 16, 632, 10.1016/j.media.2010.10.001 Padoy, 2008, On-line recognition of surgical activity for monitoring in the operating room, 1718 Quellec, 2014, Real-time recognition of surgical tasks in eye surgery videos, Med. Image Anal., 18, 579, 10.1016/j.media.2014.02.007 Quellec, 2015, Real-time task recognition in cataract surgery videos using adaptive spatiotemporal polynomials, IEEE Trans. Med. Imaging, 34, 877, 10.1109/TMI.2014.2366726 Roth, 2018, Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation, Med. Image Anal., 45, 94, 10.1016/j.media.2018.01.006 Sahu, 2017, Addressing multi-label imbalance problem of surgical tool detection using CNN, Int. J. Comput. Ass. Radiol. Surg., 1 Sarikaya, 2017, Detection and localization of robotic tools in robot-assisted surgery videos using deep neural networks for region proposal and detection, IEEE Trans. Med. Imaging, 36, 1542, 10.1109/TMI.2017.2665671 Speidel, S., Bodenstedt, S., Kenngott, H., Wagner, M., Mller-Stich, B., Maier-Hein, L., 2018. 2018 MICCAI Surgical Workflow Challenge. https://endovissub2017-workflow.grand-challenge.org/. Twinanda, 2017 Twinanda, A. P., Shehata, S., Mutter, D., Marescaux, J., de Mathelin, M., Padoy, N., 2016. Cholec80 dataset. http://camma.u-strasbg.fr/datasets. Twinanda, 2017, Endonet: a deep architecture for recognition tasks on laparoscopic videos, IEEE Trans. Med. Imaging, 36, 86, 10.1109/TMI.2016.2593957 Wang, 2017, Deep learning based multi-label classification for surgical tool presence detection in laparoscopic videos, 620 Wang, 2019, Graph convolutional nets for tool presence detection in surgical videos, 467 Wesierski, 2018, Instrument detection and pose estimation with rigid part mixtures model in video-assisted surgeries, Med. Image Anal., 46, 244, 10.1016/j.media.2018.03.012 Xue, 2017, Full quantification of left ventricle via deep multitask learning network respecting intra-and inter-task relatedness, 276 Yengera, G., Mutter, D., Marescaux, J., Padoy, N., 2018. Less is more: surgical phase recognition with less annotations through self-supervised pre-training of CNN-LSTM networks. In: arXiv preprint. UCL: 1805.08569. Yi, 2019, Hard frame detection and online mapping for surgical phase recognition Yu, 2019, Assessment of automated identification of phases in videos of cataract surgery using machine learning and deep learning techniques, JAMA Netw. Open, 2, 10.1001/jamanetworkopen.2019.1860 Zappella, 2013, Surgical gesture classification from video and kinematic data, Med. Image Anal., 17, 732, 10.1016/j.media.2013.04.007 Zhou, 2018, SFCN-OPI: Detection and fine-grained classification of nuclei using sibling FCN with objectness prior interaction Zisimopoulos, 2018, DeepPhase: Surgical phase recognition in CATARACTS videos, 265