为多器官CT分割设计了基于风险控制的自适应阈值校准方法。
Bound-Aware Per-Organ Recall Risk Control for Multi-Organ CT Segmentation under Clinical Domain Shift
- 按器官独立校准阈值，实现无分布假设下的召回率保障
- 迁移后12个器官中7个召回率超限，小样本易掩盖风险
- 新方法用25例即可重校验6个关键器官，适合临床部署
本文提出无需分布假设的风险控制方法，为冻结的分割模型添加器官特异性召回率保障。对AMOS训练的nnU-Net进行器官级阈值校准，评估其在RAOS数据集上的迁移性能，并通过病例级体素假阴性率（FNR）估算局部再认证成本。AMOS验证通过，但迁移后12个器官中有7个超过α=0.10的召回率阈值；较小的校准集可能因保守或空洞阈值掩盖风险。风险控制预测集（RCPS）提供高概率的总体均值风险控制，而共形风险控制（CRC）仅提供较弱的期望控制。两者均需交换性假设；固定全局阈值无法保证器官级效果。Waudby-Smith-Ramdas（WSR）赌注界使用25个本地病例即可重校验6个一级器官，优于霍夫丁-本特克斯（HB）所需的30–40例；CRC虽只需10–15例，但个体案例尾部风险更重。在25例条件下，无二级器官满足我们示例的精确度标准。
原文摘要 · Abstract (English)
Distribution-free risk control adds organ-specific recall guarantees to frozen segmentation. We calibrate per-organ thresholds for an AMOS-trained nnU-Net, audit transfer to RAOS, and estimate local re-certification cost using case-level voxel false-negative rate (FNR). The AMOS control passes, but $7/12$ organs exceed $α{=}0.10$ after transfer; smaller calibration sets can mask exceedances with conservative or vacuous thresholds. Risk-Controlling Prediction Sets (RCPS) give high-probability control of population-mean risk, whereas Conformal Risk Control (CRC) gives weaker expectation control. Both require exchangeability; fixed and global thresholds give no per-organ guarantee. The Waudby--Smith--Ramdas (WSR) betting bound re-certifies six Tier-1 organs with 25 local cases, versus 30--40 for Hoeffding--Bentkus (HB). CRC needs 10--15 but has a heavier individual-case tail. No Tier-2 organ meets our illustrative precision criterion with 25 cases.
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