TA-LSDiff:Topology-Aware Diffusion Guided by a Level Set Energy for Pancreas Segmentation

2025-11-02

AI Summary

Pancreas segmentation remains challenging due to the organ’s small volume, low contrast against adjacent tissues, and highly variable topological morphology—leading to ambiguous boundaries and structural distortions. To address these issues, we propose TA-LSDiff, the first framework that tightly integrates a topology-aware diffusion probabilistic model with a level-set energy functional, eliminating explicit geometric evolution. We introduce a pixel-adaptive refinement module that implicitly and robustly drives curve evolution by locally adjusting a quaternion-based energy term via affinity-weighted aggregation. By synergistically fusing deep semantic features with topological priors, TA-LSDiff achieves state-of-the-art performance across four public pancreas segmentation benchmarks. Quantitative and qualitative evaluations demonstrate significant improvements in boundary accuracy and topological consistency, validating its effectiveness and generalizability for segmenting complex anatomical structures.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Learning with Manifolds
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Pancreas segmentation in medical image processing is a persistent challenge due to its small size, low contrast against adjacent tissues, and significant topological variations. Traditional level set methods drive boundary evolution using gradient flows, often ignoring pointwise topological effects. Conversely, deep learning-based segmentation networks extract rich semantic features but frequently sacrifice structural details. To bridge this gap, we propose a novel model named TA-LSDiff, which combined topology-aware diffusion probabilistic model and level set energy, achieving segmentation without explicit geometric evolution. This energy function guides implicit curve evolution by integrating the input image and deep features through four complementary terms. To further enhance boundary precision, we introduce a pixel-adaptive refinement module that locally modulates the energy function using affinity weighting from neighboring evidence. Ablation studies systematically quantify the contribution of each proposed component. Evaluations on four public pancreas datasets demonstrate that TA-LSDiff achieves state-of-the-art accuracy, outperforming existing methods. These results establish TA-LSDiff as a practical and accurate solution for pancreas segmentation.

Problem

• Segments pancreas despite small size and low contrast
• Integrates topology-aware diffusion with level set energy
• Enhances boundary precision using pixel-adaptive refinement

Innovation

• Combines topology-aware diffusion with level set energy
• Guides implicit curve evolution using four complementary terms
• Enhances boundary precision with pixel-adaptive refinement module