Journal of medical imaging (Bellingham, Wash.) · 2026
CoReg-Net: contrastive registration for structurally consistent one-shot medical image segmentation.
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Xia Ziyi, Huang Yongzhi, Zhou Feng, Zhang Hongwei, Peng Yun, Liu Yong, Tu Liyun
Аннотация
PURPOSE: In clinical scenarios where only a single labeled reference image is available for the target segmentation task, registration-based one-shot segmentation is a practical solution through anatomical label transfer. However, existing deep learning-based registration methods are often sensitive to appearance variations, which distort anatomical correspondence in the latent feature space and lead to structurally inconsistent deformation fields. This work aims to improve the structural consistency of deformable registration by explicitly aligning inter-subject anatomical representations.
APPROACH: We propose CoReg-Net, a contrastive registration framework that integrates an inter-subject contrastive learning (ISCL) module to enforce anatomically consistent feature representations by matching spatially corresponding regions across subjects. In parallel, a cascaded skip connection (CSC) mechanism is introduced to bridge the semantic gap between hierarchical encoder and decoder features, enabling more coherent reconstruction of fine-grained deformation details. These two components are jointly optimized to guide the network toward structurally meaningful and topologically regular deformation fields for robust label propagation.
RESULTS: The proposed method demonstrates robust and consistent one-shot segmentation performance across diverse anatomical structures and imaging modalities on the IXI, OASIS, and BCV datasets. Compared with state-of-the-art registration-based baselines, CoReg-Net generates deformation fields with higher structural similarity to target images, confirming the effectiveness of contrastive feature alignment. Atlas replacement experiments further verify the robustness of the proposed method under atlas variation. Moreover, the framework exhibits adaptive deformability, preserving topological regularity in brain tissues while accurately capturing large non-rigid deformations in abdominal organs.
CONCLUSIONS: CoReg-Net shows that explicitly enforcing structural consistency through contrastive registration improves structural similarity and maintains segmentation accuracy. The proposed framework provides a reliable and generalizable solution for atlas-guided anatomical analysis across heterogeneous imaging modalities. Code is available at https://github.com/RelMono/CoRegNet.
APPROACH: We propose CoReg-Net, a contrastive registration framework that integrates an inter-subject contrastive learning (ISCL) module to enforce anatomically consistent feature representations by matching spatially corresponding regions across subjects. In parallel, a cascaded skip connection (CSC) mechanism is introduced to bridge the semantic gap between hierarchical encoder and decoder features, enabling more coherent reconstruction of fine-grained deformation details. These two components are jointly optimized to guide the network toward structurally meaningful and topologically regular deformation fields for robust label propagation.
RESULTS: The proposed method demonstrates robust and consistent one-shot segmentation performance across diverse anatomical structures and imaging modalities on the IXI, OASIS, and BCV datasets. Compared with state-of-the-art registration-based baselines, CoReg-Net generates deformation fields with higher structural similarity to target images, confirming the effectiveness of contrastive feature alignment. Atlas replacement experiments further verify the robustness of the proposed method under atlas variation. Moreover, the framework exhibits adaptive deformability, preserving topological regularity in brain tissues while accurately capturing large non-rigid deformations in abdominal organs.
CONCLUSIONS: CoReg-Net shows that explicitly enforcing structural consistency through contrastive registration improves structural similarity and maintains segmentation accuracy. The proposed framework provides a reliable and generalizable solution for atlas-guided anatomical analysis across heterogeneous imaging modalities. Code is available at https://github.com/RelMono/CoRegNet.