通过反事实上下文审计提升脑胶质瘤分割在不同机构间的鲁棒性
TRACE-Seg3D: Counterfactual Context Auditing For Robust 3D Glioma Segmentation Under Institutional Shift
- 构建反事实上下文审计框架，系统改变成像条件评估分割稳定性
- 在BraTS和UTSW数据集上实现跨域性能优于传统方法，且暴露隐藏失败模式
- 可对每例患者提供可靠性评估，超越传统重叠率指标
医学图像分割模型虽在基准测试中表现优异，却对扫描仪、协议及机构差异敏感。这些上下文变化改变图像外观但不改变病灶本质，导致模型利用无关线索，而Dice和HD95等指标难以揭示此类问题。本文提出TRACE-Seg3D，一种用于3D医学图像分割的反事实上下文审计框架。该框架保留病灶相关证据，系统性地改变成像上下文以量化预测在受控上下文偏移下的稳定性。每个分割结果均附带上下文敏感性和解剖合理性审计证据，实现病例级可靠性评估，突破传统重叠率评价局限。在BraTS和UTSW脑胶质瘤分割基准上的实验表明，该方法在分布内与跨域场景下均表现优异，并揭示了传统指标忽略的上下文敏感性失败模式。结果确立了反事实上下文审计作为实现分布偏移下透明可靠3D医学图像分割的可行路径。代码已开源：https://github.com/danleneurocom/Counterfactual-Representation-Network。
原文摘要 · Abstract (English)
Medical image segmentation models can achieve strong benchmark performance while remaining sensitive to scanner, protocol, and institutional variation. These context shifts alter image appearance without changing the underlying lesion, allowing models to exploit nuisance cues that Dice and HD95 fail to expose. We present TRACE-Seg3D, a counterfactual context auditing framework for robust 3D medical image segmentation. TRACE-Seg3D preserves lesion-relevant evidence and systematically varies imaging context to quantify prediction stability under controlled context shifts. The framework pairs each segmentation with audit evidence for context sensitivity and anatomical plausibility, enabling case-level reliability assessment beyond overlap-based evaluation. Experiments on BraTS and UTSW glioma segmentation benchmarks demonstrate competitive in-distribution and cross-domain performance. TRACE-Seg3D also exposes context-sensitive failure modes missed by conventional metrics. These results establish counterfactual context auditing as a practical route toward transparent and reliable 3D medical image segmentation under distribution shift. Our code is available at https://github.com/danleneurocom/Counterfactual-Representation-Network.
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