通过不确定性引导与解剖先验，实现胸部肿瘤精准分割。
Uncertainty-Guided Coarse-to-Fine Tumor Segmentation with Anatomy-Aware Post-Processing
- 分两阶段：先粗筛再精修，结合不确定度损失提升边界精度。
- 在Orlando数据集上Dice从0.4690提升至0.6447，假阳性显著减少。
- 适合需高可靠性肿瘤分割的临床场景，尤其边界模糊病例。
胸部CT中肿瘤分割因边界模糊、类别不平衡和解剖变异仍具挑战。本文提出一种不确定性引导的粗到细分割框架，结合全体积定位与感兴趣区域（ROI）精修，并引入解剖感知后处理。第一阶段生成粗略预测，基于肺部重叠度、距肺表面距离及组件大小进行解剖学过滤；第二阶段模型使用不确定性感知损失函数对所得ROI进行精细分割，提升模糊区域的准确性和边界校准。在私有与公开数据集上的实验表明，该方法在Dice和Hausdorff分数上均有提升，假阳性更少，空间可解释性增强。在Orlando数据集上，Swin UNETR的Dice从0.4690提升至0.6447。虚假成分减少与分割性能提升强相关，凸显解剖先验后处理的价值。
原文摘要 · Abstract (English)
Reliable tumor segmentation in thoracic computed tomography (CT) remains challenging due to boundary ambiguity, class imbalance, and anatomical variability. We propose an uncertainty-guided, coarse-to-fine segmentation framework that combines full-volume tumor localization with refined region-of-interest (ROI) segmentation, enhanced by anatomically aware post-processing. The first-stage model generates a coarse prediction, followed by anatomically informed filtering based on lung overlap, proximity to lung surfaces, and component size. The resulting ROIs are segmented by a second-stage model trained with uncertainty-aware loss functions to improve accuracy and boundary calibration in ambiguous regions. Experiments on private and public datasets demonstrate improvements in Dice and Hausdorff scores, with fewer false positives and enhanced spatial interpretability. These results highlight the value of combining uncertainty modeling and anatomical priors in cascaded segmentation pipelines for robust and clinically meaningful tumor delineation. On the Orlando dataset, our framework improved Swin UNETR Dice from 0.4690 to 0.6447. Reduction in spurious components was strongly correlated with segmentation gains, underscoring the value of anatomically informed post-processing.
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