  Unsupervised cross-modality domain adaptation is a challenging task in
medical image analysis, and it becomes more challenging when source and target
domain data are collected from multiple institutions. In this paper, we present
our solution to tackle the multi-institutional unsupervised domain adaptation
for the crossMoDA 2023 challenge. First, we perform unpaired image translation
to translate the source domain images to the target domain, where we design a
dynamic network to generate synthetic target domain images with controllable,
site-specific styles. Afterwards, we train a segmentation model using the
synthetic images and further reduce the domain gap by self-training. Our
solution achieved the 1st place during both the validation and testing phases
of the challenge. The code repository is publicly available at
https://github.com/MedICL-VU/crossmoda2023.
