Grammatikopoulou, M;
Flouty, E;
Kadkhodamohammadi, A;
Quellec, G;
Chow, A;
Nehme, J;
Luengo, I;
(2021)
CaDIS: Cataract dataset for surgical RGB-image segmentation.
Medical Image Analysis
, 71
, Article 102053. 10.1016/j.media.2021.102053.
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Abstract
Video feedback provides a wealth of information about surgical procedures and is the main sensory cue for surgeons. Scene understanding is crucial to computer assisted interventions (CAI) and to post-operative analysis of the surgical procedure. A fundamental building block of such capabilities is the identification and localization of surgical instruments and anatomical structures through semantic segmentation. Deep learning has advanced semantic segmentation techniques in the recent years but is inherently reliant on the availability of labelled datasets for model training. This paper introduces a dataset for semantic segmentation of cataract surgery videos complementing the publicly available CATARACTS challenge dataset. In addition, we benchmark the performance of several state-of-the-art deep learning models for semantic segmentation on the presented dataset. The dataset is publicly available at https://cataracts-semantic-segmentation2020.grand-challenge.org/.
| Type: | Article |
|---|---|
| Title: | CaDIS: Cataract dataset for surgical RGB-image segmentation |
| Location: | Netherlands |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.media.2021.102053 |
| Publisher version: | https://doi.org/10.1016/j.media.2021.102053 |
| Language: | English |
| Additional information: | © 2021 The Authors. Published by Elsevier B.V. under a Creative Commons license (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
| Keywords: | Cataract surgery, Dataset, Semantic segmentation |
| 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/10126591 |
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