SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal
  Segmentation
Zhusi Zhong Jie Li Lulu Bi Li Yang Ihab Kamel Rama Chellappa Xinbo Gao Harrison Bai Zhicheng Jiao
Medical image segmentation based on deep learning often fails when deployed
on images from a different domain. The domain adaptation methods aim to solve
domain-shift challenges, but still face some problems. The transfer learning
methods require annotation on the target domain, and the generative
unsupervised domain adaptation (UDA) models ignore domain-specific
representations, whose generated quality highly restricts segmentation
performance. In this study, we propose a novel Structure-Modal Constrained
(SMC) UDA framework based on a discriminative paradigm and introduce edge
structure as a bridge between domains. The proposed multi-modal learning
backbone distills structure information from image texture to distinguish
domain-invariant edge structure. With the structure-constrained self-learning
and progressive ROI, our methods segment the kidney by locating the 3D spatial
structure of the edge. We evaluated SMC-UDA on public renal segmentation
datasets, adapting from the labeled source domain (CT) to the unlabeled target
domain (CT/MRI). The experiments show that our proposed SMC-UDA has a strong
generalization and outperforms generative UDA methods.