基于减影图像与集成模型，提升乳腺肿瘤分割与治疗反应预测性能
Subtraction-Based Tumor Segmentation and Lesion-Centered pCR Prediction for the MAMA-MIA Challenge
- 用增强前后的差分图像作为输入，结合集成与后处理提升分割鲁棒性
- 分割Dice达0.713，跨中心数据下性能公平性得分0.882，表现优异
- 基于病灶中心裁剪的视频分类器集成，适合多中心医学影像分析场景
我们介绍团队FME在MAMA-MIA挑战赛中的提交方案，该挑战赛评估在多国外部队列中，从治疗前动态对比增强乳腺MRI预测原发肿瘤分割和病理完全缓解（pCR）的能力。针对分割任务，我们仅使用首次增强后减去增强前的图像，训练了五折残差编码器nnU-Net集成模型，并结合镜像测试增强与最大连通区域过滤。针对pCR预测，我们集成了25个预训练的3D视频分类器，这些分类器基于预增强和前两个增强时相的病灶中心裁剪图像训练而成。FME在两项任务中均排名第二。分割方法取得综合性能-公平性得分0.882，Dice为0.713，归一化豪斯多夫距离为0.099。pCR预测方法获得综合得分0.664，平衡准确率为0.541，等几率差异为0.212。结果表明，减影输入与集成策略能有效应对跨中心领域偏移，但仅依赖基线DCE-MRI进行pCR预测仍具局限性。
原文摘要 · Abstract (English)
We describe the submission of team FME to the MAMA-MIA Challenge, which evaluated primary tumor segmentation and prediction of pathological complete response (pCR) from pretreatment dynamic contrast-enhanced breast MRI on an external multi-country cohort. For segmentation, we trained a five-fold residual-encoder nnU-Net ensemble using only the first post-contrast minus pre-contrast image, combined with mirroring test-time augmentation and largest-connected-component filtering. For pCR prediction, we ensembled 25 pretrained 3D video classifiers trained on lesion-centred crops from the pre-contrast and first two post-contrast volumes. FME ranked second in both tasks. The segmentation method achieved a combined performance-fairness score of 0.882, with Dice 0.713 and normalized Hausdorff distance 0.099. The pCR method achieved a combined score of 0.664, balanced accuracy of 0.541, and equalized-odds disparity of 0.212. The results indicate that subtraction-based input and ensembling support robust tumor segmentation under cross-site domain shift, whereas pCR prediction from baseline DCE-MRI alone remains limited. For the submission repository, see https://github.com/FraunhoferMEVIS/MAMA-MIA-Challenge-FME
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