Assessing nnU-Net Generalization across Brain Tumor Populations in BraTS-GoAT 2026
Tristan Kirscher, Vivian Metzger, Philippe Meyer, Xavier Coubez
This paper evaluates the performance of a neural network-based tumor segmentation model on a diverse dataset of brain tumor images. Practitioners in the field of medical imaging may care about the findings as they could inform the development of more robust and generalizable models for tumor segmentation.
Abstract
BraTS-GoAT evaluates tumor segmentation across heterogeneous populations. We trained a conventional 3D nnU-Net on 1,351 labeled cases using five-fold cross-validation and 1,000 epochs per fold. The final predictor averaged all folds and applied test-time mirroring. On pooled official validation, global DSC values were 0.7805, 0.8288, and 0.8854 for enhancing tumor (ET), tumor core (TC), and whole tumor (WT). Under matched fold-0 inference, mean regional Dice decreased from 0.9058 on source out-of-fold (OOF) cases to 0.8310 on pooled validation (difference--0.0747). Mirroring gave small single-fold gains but no clear ensemble benefit; a residual-encoder alternative reached 0.8282 mean Dice. In labeled OOF predictions, failure cases had substantially smaller reference ET volumes; after adjustment for ET and WT volume, lower Dice remained associated with more disconnected ET components and a smaller fraction of ET contained in the largest component.