SurgicalSemiSeg: A Semi-Supervised Framework for Laparoscopic Image Segmentation

Yuning Zhou, Henry Badgery, Matthew Read, James Bailey, Catherine E Davey
Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, PMLR 301:1910-1929, 2026.

Abstract

Deep learning applications in surgery are heavily reliant on large-scale datasets with high-quality annotations, which are costly and time-consuming to obtain. Self-supervised learning (SSL) has shown significant potential for reducing reliance on labelled data.This work investigates the use of SSL for semantic segmentation in laparoscopic cholecystectomy (LC) surgery. Through evaluation of existing SSL methods, we find that pixel-level objectives enable the most effective representation learning for laparoscopic imaging, characterised by highly variable and deformable anatomy. Building on this insight, we develop a tailored masked denoising autoencoder with a carefully optimised masking ratio and patch size for semantic segmentation. Our method achieves state-of-the-art performance across three LC datasets. Of note, it significantly improves segmentation accuracy for critical anatomical structures that are under-represented in training datasets. Furthermore, our approach achieves generalisability, with pre-trained representations performing effectively across fine-tuning datasets from different LC datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v301-zhou26a, title = {SurgicalSemiSeg: A Semi-Supervised Framework for Laparoscopic Image Segmentation}, author = {Zhou, Yuning and Badgery, Henry and Read, Matthew and Bailey, James and Davey, Catherine E}, booktitle = {Proceedings of The 8th International Conference on Medical Imaging with Deep Learning}, pages = {1910--1929}, year = {2026}, editor = {Tasdizen, Tolga and Elhabian, Shireen and Summers, Ronald and Chen, Chen and Koch, Lisa and Zhuang, Yan}, volume = {301}, series = {Proceedings of Machine Learning Research}, month = {09--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v301/main/assets/zhou26a/zhou26a.pdf}, url = {https://proceedings.mlr.press/v301/zhou26a.html}, abstract = {Deep learning applications in surgery are heavily reliant on large-scale datasets with high-quality annotations, which are costly and time-consuming to obtain. Self-supervised learning (SSL) has shown significant potential for reducing reliance on labelled data.This work investigates the use of SSL for semantic segmentation in laparoscopic cholecystectomy (LC) surgery. Through evaluation of existing SSL methods, we find that pixel-level objectives enable the most effective representation learning for laparoscopic imaging, characterised by highly variable and deformable anatomy. Building on this insight, we develop a tailored masked denoising autoencoder with a carefully optimised masking ratio and patch size for semantic segmentation. Our method achieves state-of-the-art performance across three LC datasets. Of note, it significantly improves segmentation accuracy for critical anatomical structures that are under-represented in training datasets. Furthermore, our approach achieves generalisability, with pre-trained representations performing effectively across fine-tuning datasets from different LC datasets.} }
Endnote
%0 Conference Paper %T SurgicalSemiSeg: A Semi-Supervised Framework for Laparoscopic Image Segmentation %A Yuning Zhou %A Henry Badgery %A Matthew Read %A James Bailey %A Catherine E Davey %B Proceedings of The 8th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2026 %E Tolga Tasdizen %E Shireen Elhabian %E Ronald Summers %E Chen Chen %E Lisa Koch %E Yan Zhuang %F pmlr-v301-zhou26a %I PMLR %P 1910--1929 %U https://proceedings.mlr.press/v301/zhou26a.html %V 301 %X Deep learning applications in surgery are heavily reliant on large-scale datasets with high-quality annotations, which are costly and time-consuming to obtain. Self-supervised learning (SSL) has shown significant potential for reducing reliance on labelled data.This work investigates the use of SSL for semantic segmentation in laparoscopic cholecystectomy (LC) surgery. Through evaluation of existing SSL methods, we find that pixel-level objectives enable the most effective representation learning for laparoscopic imaging, characterised by highly variable and deformable anatomy. Building on this insight, we develop a tailored masked denoising autoencoder with a carefully optimised masking ratio and patch size for semantic segmentation. Our method achieves state-of-the-art performance across three LC datasets. Of note, it significantly improves segmentation accuracy for critical anatomical structures that are under-represented in training datasets. Furthermore, our approach achieves generalisability, with pre-trained representations performing effectively across fine-tuning datasets from different LC datasets.
APA
Zhou, Y., Badgery, H., Read, M., Bailey, J. & Davey, C.E.. (2026). SurgicalSemiSeg: A Semi-Supervised Framework for Laparoscopic Image Segmentation. Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 301:1910-1929 Available from https://proceedings.mlr.press/v301/zhou26a.html.

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