Sanchez-Matilla, R;
Robu, M;
Luengo, I;
Stoyanov, D;
(2021)
Scalable Joint Detection and Segmentation of Surgical Instruments with Weak Supervision.
In: DeBruijne, M and Cattin, PC and Cotin, S and Padoy, N and Speidel, S and Zheng, Y and Essert, C, (eds.)
International Conference on Medical Image Computing and Computer-Assisted Intervention MICCAI 2021: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021.
(pp. pp. 501-511).
Springer, Cham
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Abstract
Computer vision based models, such as object segmentation, detection and tracking, have the potential to assist surgeons intra-operatively and improve the quality and outcomes of minimally invasive surgery. Different work streams towards instrument detection include segmentation, bounding box localisation and classification. While segmentation models offer much more granular results, bounding box annotations are easier to annotate at scale. To leverage the granularity of segmentation approaches with the scalability of bounding box-based models, a multi-task model for joint bounding box detection and segmentation of surgical instruments is proposed. The model consists of a shared backbone and three independent heads for the tasks of classification, bounding box regression, and segmentation. Using adaptive losses together with simple yet effective weakly-supervised label inference, the proposed model use weak labels to learn to segment surgical instruments with a fraction of the dataset requiring segmentation masks. Results suggest that instrument detection and segmentation tasks share intrinsic challenges and jointly learning from both reduces the burden of annotating masks at scale. Experimental validation shows that the proposed model obtain comparable results to that of single-task state-of-the-art detector and segmentation models, while only requiring a fraction of the dataset to be annotated with masks. Specifically, the proposed model obtained 0.81 weighted average precision (wAP) and 0.73 mean intersection-over-union (IOU) in the Endovis2018 dataset with 1% annotated masks, while performing joint detection and segmentation at more than 20 frames per second.
| Type: | Proceedings paper |
|---|---|
| Title: | Scalable Joint Detection and Segmentation of Surgical Instruments with Weak Supervision |
| Event: | International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) |
| Location: | ELECTR NETWORK |
| Dates: | 27 September 2021 - 01 October 2021 |
| ISBN-13: | 978-3-030-87195-6 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-87196-3_47 |
| Publisher version: | https://doi.org/10.1007/978-3-030-87196-3_47 |
| 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, Software Engineering, Engineering, Biomedical, Imaging Science & Photographic Technology, Radiology, Nuclear Medicine & Medical Imaging, Computer Science, Engineering, Instrument detection, Instrument segmentation, Multi-task learning, Semi-supervised learning |
| 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/10140843 |
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