Subtraction-Based Tumor Segmentation and Lesion-Centered pCR Prediction for the MAMA-MIA Challenge
Computer Vision and Pattern RecognitionArtificial IntelligenceMachine Learning
Summary
The authors participated in a challenge to automatically find breast tumors and predict treatment success from special MRI scans taken before treatment. For finding tumors, they used a type of AI model that looks at differences between MRI images taken before and after contrast dye is added, combining several models to improve accuracy. For predicting if the treatment will completely remove the tumor, they used many AI models analyzing small 3D parts of the tumor area but found this prediction less reliable. Their tumor-finding method performed well across different countries' data, while predicting treatment success based on the MRI alone was more challenging.
Authors
Kai Geissler, Raphael Schäfer
Abstract
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