用反事实图像提升医学影像病灶区的解剖结构分割准确率
CF-Seg: Counterfactuals meet Segmentation
- 通过生成无病态下的反事实图像，模拟健康状态下的组织外观
- 在两张真实胸部X光数据集上，分割准确率显著提升
- 无需修改模型即可增强现有分割系统对病灶干扰的鲁棒性
医学影像中的解剖结构分割对疾病定量评估至关重要。然而，疾病存在时，病变区域会改变周围正常组织的外观，引入模糊边界甚至遮蔽关键结构，导致基于真实数据训练的分割模型难以准确分割，可能引发误诊。本文提出生成反事实（Counterfactual, CF）图像，模拟相同解剖结构在无病状态下的外观，而不改变其内在结构。利用这些反事实图像进行分割，无需修改原有分割模型。在两个真实临床胸部X光数据集上的实验表明，使用反事实图像可显著提升解剖结构分割性能，从而辅助下游临床决策。
原文摘要 · Abstract (English)
Segmenting anatomical structures in medical images plays an important role in the quantitative assessment of various diseases. However, accurate segmentation becomes significantly more challenging in the presence of disease. Disease patterns can alter the appearance of surrounding healthy tissues, introduce ambiguous boundaries, or even obscure critical anatomical structures. As such, segmentation models trained on real-world datasets may struggle to provide good anatomical segmentation, leading to potential misdiagnosis. In this paper, we generate counterfactual (CF) images to simulate how the same anatomy would appear in the absence of disease without altering the underlying structure. We then use these CF images to segment structures of interest, without requiring any changes to the underlying segmentation model. Our experiments on two real-world clinical chest X-ray datasets show that the use of counterfactual images improves anatomical segmentation, thereby aiding downstream clinical decision-making.
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