Al Hajj, H;
Lamard, M;
Conze, P-H;
Roychowdhury, S;
Hu, X;
Marsalkaite, G;
Zisimopoulos, O;
... Quellec, G; + view all
(2019)
CATARACTS: Challenge on automatic tool annotation for cataRACT surgery.
Medical Image Analysis
, 52
pp. 24-41.
10.1016/j.media.2018.11.008.
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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.
| Type: | Article |
|---|---|
| Title: | CATARACTS: Challenge on automatic tool annotation for cataRACT surgery |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.media.2018.11.008 |
| Publisher version: | https://doi.org/10.1016/j.media.2018.11.008 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Life Sciences & Biomedicine, Computer Science, Artificial Intelligence, Computer Science, Interdisciplinary Applications, Engineering, Biomedical, Radiology, Nuclear Medicine & Medical Imaging, Computer Science, Engineering, Cataract surgery, Video analysis, Deep learning, Challenge, SURGICAL-INSTRUMENTS, NEEDLE TRACKING, LOCALIZATION, RECOGNITION, NETWORKS, ROBOT |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10068008 |
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