Zhang, Y;
Bano, S;
Page, AS;
Deprest, J;
Stoyanov, D;
Vasconcelos, F;
(2022)
Retrieval of Surgical Phase Transitions Using Reinforcement Learning.
In: Wang, L and Dou, Q and Fletcher, PT and Speidel, S and Li, S, (eds.)
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022.
(pp. pp. 497-506).
Springer: Cham, Switzerland.
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Abstract
In minimally invasive surgery, surgical workflow segmentation from video analysis is a well studied topic. The conventional approach defines it as a multi-class classification problem, where individual video frames are attributed a surgical phase label. We introduce a novel reinforcement learning formulation for offline phase transition retrieval. Instead of attempting to classify every video frame, we identify the timestamp of each phase transition. By construction, our model does not produce spurious and noisy phase transitions, but contiguous phase blocks. We investigate two different configurations of this model. The first does not require processing all frames in a video (only <60% and <20% of frames in 2 different applications), while producing results slightly under the state-of-the-art accuracy. The second configuration processes all video frames, and outperforms the state-of-the art at a comparable computational cost. We compare our method against the recent top-performing frame-based approaches TeCNO and Trans-SVNet on the public dataset Cholec80 and also on an in-house dataset of laparoscopic sacrocolpopexy. We perform both a frame-based (accuracy, precision, recall and F1-score) and an event-based (event ratio) evaluation of our algorithms.
| Type: | Proceedings paper |
|---|---|
| Title: | Retrieval of Surgical Phase Transitions Using Reinforcement Learning |
| Event: | Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference |
| Location: | Singapore, SINGAPORE |
| Dates: | 18 Sep 2022 - 22 Sep 2022 |
| ISBN-13: | 978-3-031-16448-4 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-031-16449-1_47 |
| Publisher version: | https://doi.org/10.1007/978-3-031-16449-1_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: | Surgical workflow segmentation, Machine Learning, Laparoscopic sacrocolpopexy, Reinforcement 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/10161571 |
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