crossMoDA challenge: Evolution of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea...

The cross-Modality Domain Adaptation (crossMoDA) challenge series, initiated in 2021 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), focuses on unsupervised cross-modality segmentation, learning from contrast-enhanced T1 (ceT1) and transferring to T2 MRI. The task is an extreme example of domain shift chosen to serve as a meaningful and illustrative benchmark. From a clinical application perspective, it aims to automate Vestibular Schwannoma (VS) and cochlea segmentation on T2 scans for more cost-effective VS management. The challenge has evolved across three editions to address increasingly complex clinical scenarios: beginning with single-institution controlled data for binary segmentation (tumour and cochlea) in 2021, extending to multi-institution data with Koos grade classification in 2022, and culminating in heterogeneous routine surveillance data with intra- and extra-meatal tumour sub-segmentation in 2023. In this work, we report the findings of the 2022 and 2023 crossMoDA editions alongside a retrospective analysis of challenge progression. Successive editions demonstrate a reduction in segmentation outliers despite concurrently increasing dataset heterogeneity, suggesting that expanded training diversity improves model robustness. Notably, the 2023 winning approach generalised effectively to prior homogeneous test sets, evidencing the benefit of heterogeneous training data. However, cochlear Dice scores declined in 2023. This decrease may reflect the combined effects of increased dataset heterogeneity, resolution variability, and the added optimisation complexity of a three-class segmentation task, although the precise cause remains uncertain. Progress can and should still be made on the specific task of VS segmentation to reach clinical acceptability but as the performance of leading competitors starts to plateau, a more challenging cross-modal learning task may be beneficial to serve as a benchmarking tool in the future.