CATARACTS: Challenge on automatic tool annotation for cataRACT surgery

•The challenge on automatic tool annotation for cataract surgery is presented.•Tool usage was manually annotated for 21 tools in 50 cataract surgery videos.•Various deep learning solutions were proposed by 14 teams.•Lessons learnt from the differential analysis of these solutions are presented.•Auto...

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Veröffentlicht in:Medical image analysis Jg. 52; S. 24 - 41
Hauptverfasser: Al Hajj, Hassan, Lamard, Mathieu, Conze, Pierre-Henri, Roychowdhury, Soumali, Hu, Xiaowei, Maršalkaitė, Gabija, Zisimopoulos, Odysseas, Dedmari, Muneer Ahmad, Zhao, Fenqiang, Prellberg, Jonas, Sahu, Manish, Galdran, Adrian, Araújo, Teresa, Vo, Duc My, Panda, Chandan, Dahiya, Navdeep, Kondo, Satoshi, Bian, Zhengbing, Vahdat, Arash, Bialopetravičius, Jonas, Flouty, Evangello, Qiu, Chenhui, Dill, Sabrina, Mukhopadhyay, Anirban, Costa, Pedro, Aresta, Guilherme, Ramamurthy, Senthil, Lee, Sang-Woong, Campilho, Aurélio, Zachow, Stefan, Xia, Shunren, Conjeti, Sailesh, Stoyanov, Danail, Armaitis, Jogundas, Heng, Pheng-Ann, Macready, William G., Cochener, Béatrice, Quellec, Gwenolé
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier B.V 01.02.2019
Elsevier BV
Elsevier
Schlagworte:
Algorithms
Annotations
Cataract Extraction - instrumentation
Cataract surgery
Cataracts
Challenge
Computer Science
Deep Learning
Eye surgery
Humans
Image analysis
Image detection
Image processing
Laparoscopy
Machine learning
Medical Imaging
Motivation
Surgery
Surgical Instruments
Video analysis
Workflow
Deep learning
Cataract surgery
Video analysis
Challenge
ISSN:1361-8415, 1361-8423, 1361-8423
Online-Zugang:Volltext
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Zusammenfassung:•The challenge on automatic tool annotation for cataract surgery is presented.•Tool usage was manually annotated for 21 tools in 50 cataract surgery videos.•Various deep learning solutions were proposed by 14 teams.•Lessons learnt from the differential analysis of these solutions are presented.•Automatic annotations are almost as accurate as manual annotations. [Display omitted] Surgical tool detection is attracting increasing attention from the medical image analysis community. The goal generally is not to precisely locate tools in images, but rather to indicate which tools are being used by the surgeon at each instant. The main motivation for annotating tool usage is to design efficient solutions for surgical workflow analysis, with potential applications in report generation, surgical training and even real-time decision support. Most existing tool annotation algorithms focus on laparoscopic surgeries. However, with 19 million interventions per year, the most common surgical procedure in the world is cataract surgery. The CATARACTS challenge was organized in 2017 to evaluate tool annotation algorithms in the specific context of cataract surgery. It relies on more than nine hours of videos, from 50 cataract surgeries, in which the presence of 21 surgical tools was manually annotated by two experts. With 14 participating teams, this challenge can be considered a success. As might be expected, the submitted solutions are based on deep learning. This paper thoroughly evaluates these solutions: in particular, the quality of their annotations are compared to that of human interpretations. Next, lessons learnt from the differential analysis of these solutions are discussed. We expect that they will guide the design of efficient surgery monitoring tools in the near future.
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ISSN:1361-8415
1361-8423
1361-8423
DOI:10.1016/j.media.2018.11.008