PSC-UDA: Point-cloud Structure Constrained Unsupervised Domain Adaptation for contour-based kidney segmentation

Cross-domain medical image segmentation has gained increasing interest for its potential to reduce annotation efforts and improve clinical generalization capabilities. Domain adaptation aims to tackle the domain shift that appears in different image modalities. In cross-domain segmentation, generative models often suffer from limited accuracy due to their lack of domain-specific representations. Besides, many transfer learning approaches rely on additional manual annotations for supervision, emerging paradigms such as Unsupervised Domain Adaptation (UDA) facilitate effective knowledge transfer even when labels in the target domain are entirely absent. In this study, we propose a novel Point-cloud Structure Constrained Unsupervised Domain Adaptation (PSC-UDA) framework based on a Contour-Aware Segmentation (CAS) model with a 3D contour point cloud to bridge the domain gaps appearing in cross-site and cross-domain medical images. The CAS model distills the domain-invariant kidney structure from image texture to distinguish the point cloud and characterize the kidney contour in a coarse-to-fine way. With point-to-voxel self-learning on 3D structure constraints, the proposed PSC-UDA framework addresses visual domain shift, adapting discriminative information of the kidney from the labeled source domain (CT) to the unlabeled target domain (CT/MRI), so that it realizes precise cross-domain kidney segmentation with limited labels. Experimental results prove that the proposed method outperforms the generative UDA methods and the source-free methods on three cross-domain kidney segmentation datasets, outperforming even without a target domain adaptation strategy. The source code is available at https://github.com/zzs95/PSC-UDA .