SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal Segmentation
This work addresses domain adaptation for medical image segmentation, which is incremental as it builds on existing UDA methods by incorporating edge structure constraints.
The authors tackled the problem of domain shift in medical image segmentation by proposing a structure-modal constrained unsupervised domain adaptation framework, which outperformed generative UDA methods in cross-domain renal segmentation from CT to CT/MRI.
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.