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Enhancing weakly supervised 3D medical image segmentation through probabilistic-aware Learning
Jiang, Runmin Fan, Zhaoxin Wu, Junhao Zhu, Lenghan Huang, Xin Wang, Tianyang Huang, Heng Xu, Min
Jiang, Runmin
Fan, Zhaoxin
Wu, Junhao
Zhu, Lenghan
Huang, Xin
Wang, Tianyang
Huang, Heng
Xu, Min
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Computer Vision
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Journal article
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http://creativecommons.org/licenses/by/4.0/
Language
English
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Abstract
3D medical image segmentation is a challenging task with crucial implications for disease diagnosis and treatment planning. Recent advances in deep learning have significantly enhanced fully supervised medical image segmentation. However, this approach heavily relies on labor-intensive and time-consuming fully annotated ground-truth labels, particularly for 3D volumes. To overcome this limitation, we propose a novel probabilistic-aware weakly supervised learning pipeline, specifically designed for 3D medical imaging. Our pipeline integrates three innovative components: a Probability-based Pseudo-Label Generation technique for synthesizing dense segmentation masks from sparse annotations, a Probabilistic Multi-head Self-Attention network for robust feature extraction within our Probabilistic Transformer Network, and a Probability-informed Segmentation Loss Function to enhance training with annotation confidence. Demonstrating significant advances, our approach not only rivals the performance of fully supervised methods but also surpasses existing weakly supervised methods in CT and MRI datasets, achieving up to 18.1% improvement in Dice scores for certain organs. The code is available at https://github.com/runminjiang/PW4MedSeg.
Citation
R. Jiang, Z. Fan, J. Wu, L. Zhu, X. Huang, T. Wang , et al., "Enhancing weakly supervised 3D medical image segmentation through probabilistic-aware Learning," Pattern Recognition, vol. 180, pp. 113973-113973, 2026, https://doi.org/10.1016/j.patcog.2026.113973.
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Pattern Recognition
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Keywords
46 Information and Computing Sciences, 4611 Machine Learning
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Publisher
Elsevier