用拓扑感知扩散模型实现胰腺精准分割，兼顾结构细节与边界精度。
TA-LSDiff:Topology-Aware Diffusion Guided by a Level Set Energy for Pancreas Segmentation
- 融合深度特征与水平集能量，隐式驱动轮廓演化，无需显式几何更新。
- 在4个公开数据集上达到最佳性能，边界精度显著优于现有方法。
- 适合需要高精度胰腺分割的临床医学图像分析场景。
胰腺在医学图像分割中面临尺寸小、与邻近组织对比度低及拓扑变化大等挑战。传统水平集方法依赖梯度流驱动边界演化，常忽略点级拓扑影响；而深度学习网络虽提取丰富语义特征，却易丢失结构细节。为此，我们提出TA-LSDiff模型，结合拓扑感知扩散概率模型与水平集能量函数，实现无需显式几何演化的分割。该能量函数通过四个互补项融合输入图像与深层特征，引导隐式曲线演化。为进一步提升边界精度，引入像素自适应精修模块，利用邻域证据进行亲和加权，局部调节能函数。消融实验系统量化各组件贡献。在四个公开胰腺数据集上的评估表明，TA-LSDiff达到当前最优准确率，验证其作为实用且高精度胰腺分割方案的有效性。
原文摘要 · Abstract (English)
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.
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