Bridging the Inter-Domain Gap through Low-Level Features for Cross-Modal Medical Image Segmentation

This paper addresses cross-modal medical image segmentation, focusing on MRI-CT transfer in a source-only domain generalization setting. During training, only source-modality samples are available, while unlabeled target-modality images are used for testing. We propose LowBridge, which builds on the observation that cross-modal images share similar low-level features (e.g. edges) as they depict the same types of anatomical structures. Specifically, we first train a generative model to recover the source images from their edge features, followed by training a segmentation model on the generated source images, separately. At test time, edge features from the target images are input to the pretrained generative model to generate source-style target domain images, which are then segmented using the pretrained segmentation network. Experiments on various public datasets demonstrate that LowBridge achieves state-of-the-art performance, outperforming ten existing approaches. Ablation studies further show that LowBridge is compatible with different types of generative and segmentation models, suggesting its generalizability and potential to benefit from future advances in these models. The code will be available at https://github.com/JoshuaLPF/LowBridge.