ASC: Appearance and Structure Consistency for Unsupervised Domain
  Adaptation in Fetal Brain MRI Segmentation
Zihang Xu Haifan Gong Xiang Wan Haofeng Li
Automatic tissue segmentation of fetal brain images is essential for the
quantitative analysis of prenatal neurodevelopment. However, producing
voxel-level annotations of fetal brain imaging is time-consuming and expensive.
To reduce labeling costs, we propose a practical unsupervised domain adaptation
(UDA) setting that adapts the segmentation labels of high-quality fetal brain
atlases to unlabeled fetal brain MRI data from another domain. To address the
task, we propose a new UDA framework based on Appearance and Structure
Consistency, named ASC. We adapt the segmentation model to the appearances of
different domains by constraining the consistency before and after a
frequency-based image transformation, which is to swap the appearance between
brain MRI data and atlases. Consider that even in the same domain, the fetal
brain images of different gestational ages could have significant variations in
the anatomical structures. To make the model adapt to the structural variations
in the target domain, we further encourage prediction consistency under
different structural perturbations. Extensive experiments on FeTA 2021
benchmark demonstrate the effectiveness of our ASC in comparison to
registration-based, semi-supervised learning-based, and existing UDA-based
methods.