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

TL;DR

TA-LSDiff is a novel pancreas segmentation method that combines topology-aware diffusion with level set energy and a pixel-adaptive refinement to improve accuracy without explicit geometric evolution.

Contribution

It introduces a topology-aware diffusion probabilistic model integrated with level set energy and a pixel-adaptive module for improved pancreas segmentation.

Findings

Achieves state-of-the-art accuracy on four public datasets.

Outperforms existing pancreas segmentation methods.

Effectively balances semantic features and structural details.

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.