让医学影像分割结果直接反映多位医生的共识程度。
TwinTrack: Post-hoc Multi-Rater Calibration for Medical Image Segmentation
- 用多位医生标注的平均比例校准模型输出概率
- 在CURVAS-PDACVI数据集上显著提升校准度
- 适合需要解释性医疗决策的场景
增强CT图像上胰腺导管腺癌（PDAC）分割具有内在模糊性：专家间标注差异反映的是真实不确定性而非标注噪声。标准深度学习方法假设存在单一真实标签，导致概率输出校准不佳且难以解释。本文提出TwinTrack框架，通过后处理校准集成分割概率，使其匹配经验性的人类平均响应（MHR）——即标记某个体素为肿瘤的专家占比。校准后的概率可直接解读为预期标注者中赋予肿瘤标签的比例，显式建模了专家间分歧。该后处理校准方法简单，仅需少量多标注者校准数据集，且在MICCAI 2025 CURVAS-PDACVI多标注者基准测试中持续优于标准方法。
原文摘要 · Abstract (English)
Pancreatic ductal adenocarcinoma (PDAC) segmentation on contrast-enhanced CT is inherently ambiguous: inter-rater disagreement among experts reflects genuine uncertainty rather than annotation noise. Standard deep learning approaches assume a single ground truth, producing probabilistic outputs that can be poorly calibrated and difficult to interpret under such ambiguity. We present TwinTrack, a framework that addresses this gap through post-hoc calibration of ensemble segmentation probabilities to the empirical mean human response (MHR) -the fraction of expert annotators labeling a voxel as tumor. Calibrated probabilities are thus directly interpretable as the expected proportion of annotators assigning the tumor label, explicitly modeling inter-rater disagreement. The proposed post-hoc calibration procedure is simple and requires only a small multi-rater calibration set. It consistently improves calibration metrics over standard approaches when evaluated on the MICCAI 2025 CURVAS-PDACVI multi-rater benchmark.
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