Tan, C;
Hu, Y;
Saeed, SU;
(2025)
SPARS: Self-Play Adversarial Reinforcement Learning for Segmentation of Liver Tumours.
In: Ali, S and Hogg, DC and Peckham, M, (eds.)
Medical Image Understanding and Analysis: 29th Annual Conference, MIUA 2025, Leeds, UK, July 15–17, 2025, Proceedings, Part III.
(pp. pp. 59-72).
Springer: Cham, Switzerland.
![]() |
Text
SPARS___MIUA_25-3.pdf - Accepted Version Access restricted to UCL open access staff until 16 July 2026. Download (2MB) |
Abstract
Accurate tumour segmentation is vital for various targeted diagnostic and therapeutic procedures for cancer, e.g., planning biopsies or tumour ablations. Manual delineation is extremely labour-intensive, requiring substantial expert time. Fully-supervised machine learning models aim to automate such localisation tasks, but require a large number of costly and often subjective 3D voxel-level labels for training. The high-variance and subjectivity in such labels impacts model generalisability, even when large datasets are available. Histopathology labels may offer more objective labels but the infeasibility of acquiring pixel-level annotations to develop tumour localisation methods based on histology remains challenging in-vivo. In this work, we propose a novel weakly-supervised semantic segmentation framework called SPARS (Self-Play Adversarial Reinforcement Learning for Segmentation), which utilises an object presence classifier, trained on a small number of image-level binary cancer presence labels, to localise cancerous regions on CT scans. Such binary labels of patient-level cancer presence can be sourced more feasibly from biopsies and histopathology reports, enabling a more objective cancer localisation on medical images. Evaluating with real patient data, we observed that SPARS yielded a mean dice score of 77.3±9.4, which outperformed other weakly-supervised methods by large margins. This performance was comparable with recent fully-supervised methods that require voxel-level annotations. Our results demonstrate the potential of using SPARS to reduce the need for extensive human-annotated labels to detect cancer in real-world healthcare settings. Code: https://github.com/catalinatan/SPARS.
Type: | Proceedings paper |
---|---|
Title: | SPARS: Self-Play Adversarial Reinforcement Learning for Segmentation of Liver Tumours |
Event: | Medical Image Understanding and Analysis (MIUA 2025) |
ISBN-13: | 9783031986932 |
DOI: | 10.1007/978-3-031-98694-9_5 |
Publisher version: | https://doi.org/10.1007/978-3-031-98694-9_5 |
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. |
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/10212299 |
Archive Staff Only
![]() |
View Item |