  Domain Adaptation (DA) has recently raised strong interests in the medical
imaging community. While a large variety of DA techniques has been proposed for
image segmentation, most of these techniques have been validated either on
private datasets or on small publicly available datasets. Moreover, these
datasets mostly addressed single-class problems. To tackle these limitations,
the Cross-Modality Domain Adaptation (crossMoDA) challenge was organised in
conjunction with the 24th International Conference on Medical Image Computing
and Computer Assisted Intervention (MICCAI 2021). CrossMoDA is the first large
and multi-class benchmark for unsupervised cross-modality DA. The challenge's
goal is to segment two key brain structures involved in the follow-up and
treatment planning of vestibular schwannoma (VS): the VS and the cochleas.
Currently, the diagnosis and surveillance in patients with VS are performed
using contrast-enhanced T1 (ceT1) MRI. However, there is growing interest in
using non-contrast sequences such as high-resolution T2 (hrT2) MRI. Therefore,
we created an unsupervised cross-modality segmentation benchmark. The training
set provides annotated ceT1 (N=105) and unpaired non-annotated hrT2 (N=105).
The aim was to automatically perform unilateral VS and bilateral cochlea
segmentation on hrT2 as provided in the testing set (N=137). A total of 16
teams submitted their algorithm for the evaluation phase. The level of
performance reached by the top-performing teams is strikingly high (best median
Dice - VS:88.4%; Cochleas:85.7%) and close to full supervision (median Dice -
VS:92.5%; Cochleas:87.7%). All top-performing methods made use of an
image-to-image translation approach to transform the source-domain images into
pseudo-target-domain images. A segmentation network was then trained using
these generated images and the manual annotations provided for the source
image.
