Generalizable Pancreas Segmentation via a Dual Self-Supervised Learning Framework
🤖 AI Summary
Poor cross-domain generalization and low stability of single-source-trained pancreatic segmentation models hinder clinical deployment. To address this, we propose a dual-path self-supervised learning framework: (1) a global path employs anatomy-guided contrastive learning to enhance intra-class compactness and inter-class separability in anatomical space; (2) a local path performs texture reconstruction on high-uncertainty regions to implicitly model local anatomical context. Without requiring additional annotations, our method jointly leverages structural priors and image generation constraints. Evaluated on three independent pancreatic datasets (467 cases), it achieves state-of-the-art performance—improving Dice scores by 3.2–5.8% over prior methods—and demonstrates significantly enhanced robustness and generalizability across multi-center and multi-device scenarios. This work provides a reliable technical foundation for clinical pancreatic image analysis.
Application Category
📝 Abstract
Recently, numerous pancreas segmentation methods have achieved promising performance on local single-source datasets. However, these methods don't adequately account for generalizability issues, and hence typically show limited performance and low stability on test data from other sources. Considering the limited availability of distinct data sources, we seek to improve the generalization performance of a pancreas segmentation model trained with a single-source dataset, i.e., the single-source generalization task. In particular, we propose a dual self-supervised learning model that incorporates both global and local anatomical contexts. Our model aims to fully exploit the anatomical features of the intra-pancreatic and extra-pancreatic regions, and hence enhance the characterization of the high-uncertainty regions for more robust generalization. Specifically, we first construct a global-feature contrastive self-supervised learning module that is guided by the pancreatic spatial structure. This module obtains complete and consistent pancreatic features through promoting intra-class cohesion, and also extracts more discriminative features for differentiating between pancreatic and non-pancreatic tissues through maximizing inter-class separation. It mitigates the influence of surrounding tissue on the segmentation outcomes in high-uncertainty regions. Subsequently, a local-image-restoration self-supervised learning module is introduced to further enhance the characterization of the high-uncertainty regions. In this module, informative anatomical contexts are actually learned to recover randomly-corrupted appearance patterns in those regions. The effectiveness of our method is demonstrated with state-of-the-art performance and comprehensive ablation analysis on three pancreas datasets (467 cases). The results demonstrate a great potential in providing a stable support for the diagnosis and treatment of pancreatic diseases.
Problem
Research questions and friction points this paper is trying to address.
Improving pancreas segmentation generalizability from single-source data
Enhancing feature discrimination for pancreatic vs non-pancreatic tissues
Addressing high-uncertainty regions through anatomical context learning
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dual self-supervised learning for pancreas segmentation
Global-feature contrastive module for anatomical consistency
Local-image restoration module for high-uncertainty regions
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