用形状先验提升心脏多腔室分割，发现现有方法效果有限。
Evaluation of Anatomical Shape Priors in Deep Learning-Based Cardiac Multi-Compartment Segmentation
- 引入形状感知损失和热力图引导的U-Net改进模型
- 标准3D U-Net表现优异，先验改善不明显且不稳定
- 提示未来需更复杂的可学习先验而非手工设计形状约束
全心多腔室CT分割在临床中至关重要，但标准卷积神经网络（CNN）未显式强制解剖合理性。基于训练数据统计，本文评估了轻量级显式形状先验（通过形状感知损失和空间标签分布热力图引导的U-Net变体实现）在MM-WHS CT和WHS++数据集上的3D心脏分割性能。所有实验结果表明，标准3D U-Net表现依然强劲，手工设计的先验仅带来微弱且不一致的改进，甚至常导致性能下降。这说明基线模型已隐式捕捉大量解剖规律，未来提升可能需要更具表达力的可学习先验，而非简单手工形状约束。
原文摘要 · Abstract (English)
Whole-heart multi-compartment CT segmentation is clinically important, but standard CNNs do not explicitly enforce anatomical plausibility. Based on statistics derived from the training data, we evaluate whether lightweight explicit shape priors, implemented as shape-aware losses and spatial label distribution heatmap-guided U-Net variants, improve 3D cardiac segmentation on MM-WHS CT and WHS++. Across all experiments, a standard 3D U-Net surprisingly remained a very strong baseline, with handcrafted priors yielding at best marginal and inconsistent changes and often degrading performance. These results suggest that the baseline already captures substantial implicit anatomical regularities and that future gains will likely require more expressive learned priors rather than simple handcrafted anatomical shape constraints.
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