用薛定谔桥模型提升医学图像模糊分割精度
Ambiguous Medical Image Segmentation Using Diffusion Schrödinger Bridge
- 引入薛定谔桥建模图像与掩码联合动态
- 在多个数据集上达到领先分割效果
- 新指标量化医生间标注差异，兼顾多样性与一致性
由于病灶边界模糊和掩码差异大，医学图像精准分割仍具挑战。本文提出首个将薛定谔桥用于模糊医学图像分割的方法——分割薛定谔桥（SSB），通过建模图像与掩码的联合动态来提升性能。SSB保持结构完整性，在无额外指导情况下清晰勾勒模糊边界，并通过新颖损失函数维持分割多样性。我们进一步提出多样性差异指数（D_DDI）以量化不同医师标注间的变异性，同时捕捉多样性与共识。SSB在LIDC-IDRI、COCA及自研RACER数据集上均达到当前最优表现。
原文摘要 · Abstract (English)
Accurate segmentation of medical images is challenging due to unclear lesion boundaries and mask variability. We introduce \emph{Segmentation Schödinger Bridge (SSB)}, the first application of Schödinger Bridge for ambiguous medical image segmentation, modelling joint image-mask dynamics to enhance performance. SSB preserves structural integrity, delineates unclear boundaries without additional guidance, and maintains diversity using a novel loss function. We further propose the \emph{Diversity Divergence Index} ($D_{DDI}$) to quantify inter-rater variability, capturing both diversity and consensus. SSB achieves state-of-the-art performance on LIDC-IDRI, COCA, and RACER (in-house) datasets.
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