CATARACTS: Challenge on automatic tool annotation for cataRACT surgery

Agustinos, 2015, 2D/3D real-time tracking of surgical instruments based on endoscopic image processing, 90 Al Hajj, 2017, Surgical tool detection in cataract surgery videos through multi-image fusion inside a convolutional neural network, 2002 Al Hajj, 2017, Smart data augmentation for surgical tool detection on the surgical tray, 4407 Al Hajj, 2018, Monitoring tool usage in surgery videos using boosted convolutional and recurrent neural networks, Med. Image Anal., 47, 203, 10.1016/j.media.2018.05.001 Allan, 2018, 3-D pose estimation of articulated instruments in robotic minimally invasive surgery, IEEE Trans. Med. Imaging, 37, 1204, 10.1109/TMI.2018.2794439 Alsheakhali, 2016, Detection of articulated instruments in retinal microsurgery, 107 Alsheakhali, 2016, CRF-based model for instrument detection and pose estimation in retinal microsurgery, Comput. Math. Methods Med., 2016, 1067509, 10.1155/2016/1067509 Attia, 2017, Surgical tool segmentation using a hybrid deep CNN-RNN auto encoder-decoder, 3373 Bernal, 2017, Comparative validation of polyp detection methods in video colonoscopy: results from the MICCAI 2015 endoscopic vision challenge, IEEE Trans. Med. Imaging, 36, 1231, 10.1109/TMI.2017.2664042 Bodenstedt, 2016, Superpixel-based structure classification for laparoscopic surgery, 978618 Bodenstedt, 2017, Unsupervised Temporal Context Learning Using Convolutional Neural Networks for Laparoscopic Workflow Analysis 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 Casals, 1996, Automatic guidance of an assistant robot in laparoscopic surgery, 1, 895 Chang, 2016, Robust catheter and guidewire tracking using B-spline tube model and pixel-wise posteriors, IEEE Robot Autom. Lett., 1, 303, 10.1109/LRA.2016.2517821 Charrière, 2017, Real-time analysis of cataract surgery videos using statistical models, Multimed. Tools Appl., 76, 22473, 10.1007/s11042-017-4793-8 Chen, 2017, Surgical instruments tracking based on deep learning with lines detection and spatio-temporal context, 2711 Chmarra, 2007, Systems for tracking minimally invasive surgical instruments, Minim. Invasive Ther. Allied Technol., 16, 328, 10.1080/13645700701702135 Cho, 2014, On the properties of neural machine translation: encoder-decoder approaches, 103 Cochener, 2018, A comparative evaluation of a new generation of diffractive trifocal and extended depth of focus intraocular lenses, J. Refract. Surg., 34, 507, 10.3928/1081597X-20180530-02 Czajkowska, 2018, Biopsy needle tracking technique in US images, Comput. Med. Imaging Graph, 65, 93, 10.1016/j.compmedimag.2017.07.001 DeLong, 1988, Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach, Biometrics, 44, 837, 10.2307/2531595 Donahue, 2017, Long-term recurrent convolutional networks for visual recognition and description, IEEE Trans. Pattern Anal. Mach. Intell., 39, 677, 10.1109/TPAMI.2016.2599174 Du, 2016, Combined 2D and 3D tracking of surgical instruments for minimally invasive and robotic-assisted surgery, Int. J. Comput. Assist. Radiol. Surg., 11, 1109, 10.1007/s11548-016-1393-4 Du, 2018, Articulated multi-instrument 2-D pose estimation using fully convolutional networks, IEEE Trans. Med. Imaging, 37, 1276, 10.1109/TMI.2017.2787672 Erie, 2014, Rising cataract surgery rates: demand and supply, Ophthalmol, 121, 2, 10.1016/j.ophtha.2013.10.002 Frikha, 2016, Handling occlusion in augmented reality surgical training based instrument tracking Funke, 2018, Generative adversarial networks for specular highlight removal in endoscopic images, 1057604 Furtado, 2016, Low resolution tool tracking for microsurgical training in a simulated environment, 3843 García-Peraza-Herrera, 2017, ToolNet: Holistically-nested real-time segmentation of robotic surgical tools, 5717 García-Peraza-Herrera, 2016, Real-time segmentation of non-rigid surgical tools based on deep learning and tracking, 84 Gessert, 2018, A deep learning approach for pose estimation from volumetric OCT data, Med. Image Anal., 46, 162, 10.1016/j.media.2018.03.002 He, 2016, Deep residual learning for image recognition, 770 Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735 Hu, 2017, AGNet: attention-guided network for surgical tool presence detection, 186 Huang, 2017, Densely connected convolutional networks, 2261 Iandola, 2016, SqueezeNet: AlexNet-Level Accuracy with 50x Fewer Parameters and <0.5MB Model Size Jin, 2018, Tool detection and operative skill assessment in surgical videos using region-based convolutional neural networks, 691 Jin, 2016, EndoRCN: Recurrent Convolutional Networks for Recognition of Surgical Workflow in Cholecystectomy Procedure Video Jordan, 1999, An introduction to variational methods for graphical models, Mach. Learn., 37, 183, 10.1023/A:1007665907178 Kaplowitz, 2018, A review of teaching methods and outcomes of resident phacoemulsification, Surv. Ophthalmol., 63, 257, 10.1016/j.survophthal.2017.09.006 Keller, 2018, Real-time corneal segmentation and 3D needle tracking in intrasurgical OCT, Biomed. Opt. Express, 9, 2716, 10.1364/BOE.9.002716 Kelman, 1967, Phaco-emulsification and aspiration. A new technique of cataract removal. A preliminary report, Am. J. Ophthalmol., 64, 23, 10.1016/0002-9394(67)93340-5 Kessel, 2016, Indication for cataract surgery. do we have evidence of who will benefit from surgery? A systematic review and meta-analysis, Acta Ophthalmol., 94, 10, 10.1111/aos.12758 Kingma, 2015, Adam: a method for stochastic optimization Ko, 2005, Intelligent interaction between surgeon and laparoscopic assistant robot system, 60 Krupa, 2003, Autonomous 3-D positioning of surgical instruments in robotized laparoscopic surgery using visual servoing, IEEE Trans. Robot. Autom., 19, 842, 10.1109/TRA.2003.817086 Krwawicz, 1961, Intracapsular extraction of intumescent cataract by application of low temperature, Br. J. Ophthalmol., 45, 279, 10.1136/bjo.45.4.279 Kügler, 2018, Instrument pose estimation using registration for otobasis surgery, 105 Kurmann, 2017, Simultaneous recognition and pose estimation of instruments in minimally invasive surgery, 505 Laina, 2017, Concurrent segmentation and localization for tracking of surgical instruments, 664 Lee, 2017, Mixed reality support for orthopaedic surgery, 204 Lee, 2017, Multi-modal imaging, model-based tracking, and mixed reality visualisation for orthopaedic surgery, Healthc. Technol. Lett., 4, 168, 10.1049/htl.2017.0066 Leppänen, 2018, Augmenting microsurgical training: Microsurgical instrument detection using convolutional neural networks, 211 Litjens, 2017, A survey on deep learning in medical image analysis, Med. Image Anal., 42, 60, 10.1016/j.media.2017.07.005 Loshchilov, 2017, SGDR: Stochastic gradient descent with warm restarts Maier-Hein, 2018, Is the Winner Really the Best? A Critical Analysis of Common Research Practice in Biomedical Image Analysis Competitions Marban, 2017, Estimating position & velocity in 3D space from monocular video sequences using a deep neural network, 1460 Mishra, 2017, Learning latent temporal connectionism of deep residual visual abstractions for identifying surgical tools in laparoscopy procedures, 2233 Miyawaki, 2009, Development of automatic acquisition system of surgical-instrument informantion in endoscopic and laparoscopic surgey, 3058 Niemeijer, 2010, Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs, IEEE Trans. Med. Imaging, 29, 185, 10.1109/TMI.2009.2033909 Olson, 2018, Cataract surgery from 1918 to the present and future - just imagine!, Am. J. Ophthalmol., 185, 10, 10.1016/j.ajo.2017.08.020 Pleiss, 2017, Memory-efficient implementation of DenseNets Popovic, 2016, Efficacy and safety of femtosecond laser-assisted cataract surgery compared with manual cataract surgery: A meta-analysis of 14 567 Eyes, Ophthalmol, 123, 2113, 10.1016/j.ophtha.2016.07.005 Primus, 2016, Temporal segmentation of laparoscopic videos into surgical phases Quellec, 2017, Multiple-instance learning for medical image and video analysis, IEEE Rev. Biomed. Eng., 10, 213, 10.1109/RBME.2017.2651164 Quellec, 2017, Deep image mining for diabetic retinopathy screening, Med. Image Anal., 39, 178, 10.1016/j.media.2017.04.012 Raju, 2016, M2CAI Surgical Tool Detection Challenge Report Rathinam, 2017, A review of image processing leading to artificial intelligence methods to detect instruments in ultrasound guided minimally invasive surgical procedures, 3074 Riaz, 2006, Surgical interventions for age-related cataract, Cochrane Database Syst. Rev., CD001323 Rieke, 2016, Real-time localization of articulated surgical instruments in retinal microsurgery, Med. Image Anal., 34, 82, 10.1016/j.media.2016.05.003 Rieke, 2016, Real-time online adaption for robust instrument tracking and pose estimation, 422 Ross, 2018, Exploiting the potential of unlabeled endoscopic video data with self-supervised learning, Int. J. Comput. Assist Radiol. Surg., 13, 925, 10.1007/s11548-018-1772-0 Ryu, 2018, A Kalman-Filter-Based common algorithm approach for object detection in surgery scene to assist surgeon’s situation awareness in robot-assisted laparoscopic surgery, J. Healthc. Eng., 2018, 8079713, 10.1155/2018/8079713 Sahu, 2016, Instrument state recognition and tracking for effective control of robotized laparoscopic systems, Int. J. Mech. Eng. Robot Res., 5, 33 Sahu, 2016, Tool and Phase Recognition Using Contextual CNN Features Sahu, 2017, Addressing multi-label imbalance problem of surgical tool detection using CNN, Int. J. Comput. Assist Radiol. Surg., 12, 1013, 10.1007/s11548-017-1565-x 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 Seward, 1997, Folding intraocular lenses: Materials and methods, Br. J. Ophthalmol., 81, 340, 10.1136/bjo.81.5.340 de Silva, 2016, Multifocal versus monofocal intraocular lenses after cataract extraction, Cochrane Database Syst Rev Simonyan, 2015, Very deep convolutional networks for large-scale image recognition Su, 2018, Real-time vision-based surgical tool segmentation with robot kinematics prior, 1 Szegedy, 2017, Inception-v4, Inception-ResNet and the impact of residual connections on learning, 4278 Szegedy, 2016, Rethinking the inception architecture for computer vision, 2818 Twinanda, 2016, Single- and Multi-Task Architectures for Tool Presence Detection Challenge at M2CAI 2016 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, 2016, Cataract surgical rate and socioeconomics: a global study, Invest. Ophthalmol. Vis. Sci., 57, 5872, 10.1167/iovs.16-19894 Wesierski, 2017, Surgical tool tracking by on-line selection of structural correlation filters, 2334 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 Yao, 2015, Describing videos by exploiting temporal structure, 4507 Ye, 2016, Real-time 3D tracking of articulated tools for robotic surgery, 386 Yosinski, 2014 Zhao, 2017, Tracking-by-detection of surgical instruments in minimally invasive surgery via the convolutional neural network deep learning-based method, Comput. Assist. Surg. (Abingdon), 22, 26, 10.1080/24699322.2017.1378777 Zhou, 2017, Needle segmentation in volumetric optical coherence tomography images for ophthalmic microsurgery, Appl. Sci., 7, 748, 10.3390/app7080748 Zia, 2016, Fine-Tuning Deep Architectures for Surgical Tool Detection Zoph, 2018, Learning transferable architectures for scalable image recognition