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CATARACTS: Challenge on automatic tool annotation for cataRACT surgery.

Authors :
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
Source :
Medical Image Analysis. Feb2019, Vol. 52, p24-41. 18p.
Publication Year :
2019

Abstract

Highlights • 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. Graphical abstract Abstract 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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13618415
Volume :
52
Database :
Academic Search Index
Journal :
Medical Image Analysis
Publication Type :
Academic Journal
Accession number :
134151882
Full Text :
https://doi.org/10.1016/j.media.2018.11.008