Assessing nnU-Net Generalization across Brain Tumor Populations in BraTS-GoAT 2026

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arxiv:2609.15524

Published on Sep 14

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Tristan on Sep 17

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Authors:

Tristan Kirscher ,

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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.

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Kirscher

Paper author Paper submitter 6 days ago

We study the generalization of a standard 3D nnU-Net across the heterogeneous populations of BraTS-GoAT. Performance dropped from 0.9058 mean Dice on source out-of-fold cases to 0.8310 on pooled validation data, with smaller and more fragmented tumors being particularly challenging. Future work could focus on improving robustness to these morphological and population shifts.

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5 days ago

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