Zhao, Shifang;
Bai, Long;
Yuan, Kun;
Li, Feng;
Yu, Jieming;
Dong, Wenzhen;
Wang, Guankun;
... Ren, Hongliang; + view all
(2025)
Rethinking data imbalance in class incremental surgical instrument segmentation.
Medical Image Analysis
, 105
, Article 103728. 10.1016/j.media.2025.103728.
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Abstract
In surgical instrument segmentation, the increasing variety of instruments over time poses a significant challenge for existing neural networks, as they are unable to effectively learn such incremental tasks and suffer from catastrophic forgetting. When learning new data, the model experiences a sharp performance drop on previously learned data. Although several continual learning methods have been proposed for incremental understanding tasks in surgical scenarios, the issue of data imbalance often leads to a strong bias in the segmentation head, resulting in poor performance. Data imbalance can occur in two forms: (i) class imbalance between new and old data, and (ii) class imbalance within the same time point of data. Such imbalances often cause the dominant classes to take over the training process of continual semantic segmentation (CSS). To address this issue, we propose SurgCSS, a novel plug-and-play CSS framework for surgical instrument segmentation under data imbalance. Specifically, we generate realistic surgical backgrounds through inpainting and blend instrument foregrounds with the generated backgrounds in a class-aware manner to balance the data distribution in various scenarios. We further propose the Class Desensitization Loss by employing contrastive learning to correct edge biases caused by data imbalance. Moreover, we dynamically fuse the weight parameters of the old and new models to achieve a better trade-off between the biased and unbiased model weights. To investigate the data imbalance problem in surgical scenarios, we construct a new benchmark for surgical instrument CSS by integrating four public datasets: EndoVis 2017, EndoVis 2018, CholecSeg8k, and SAR-RAPR50. Extensive experiments demonstrate the effectiveness of the proposed framework, achieving significant performance improvement against existing baselines. Our method demonstrates excellent potential for clinical applications. The code is publicly available at github.com/Zzsf11/SurgCS
| Type: | Article |
|---|---|
| Title: | Rethinking data imbalance in class incremental surgical instrument segmentation |
| Location: | Netherlands |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.media.2025.103728 |
| Publisher version: | https://doi.org/10.1016/j.media.2025.103728 |
| Language: | English |
| Additional information: | © The Author(s), 2025. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
| Keywords: | Continual learning, Data imbalance, Data synthesis, Instrument segmentation, Robotic surgery |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10212110 |
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