CE-UDA - A Cross-Modal Domain Adaptation Model for Cochlea and Vestibular Schwannoma Segmentation Based on Contour Enhancement

Accurate identification and segmentation of the cochlea is crucial in the diagnosis and treatment of Vestibular Schwannoma (VS). High-resolution T2-weighted (hrT2) imaging is safer, less expensive, and provides more detailed soft tissue diagnostic information than the commonly used contrast-enhanced T1 (ceT1) imaging. However, acquiring pixel-level hrT2 expert annotation data is costly and time-consuming, limiting its data availability. To this end, we propose an unsupervised domain adaptation model, CE-UDA, and design the Contour Enhancement Module (CEM) module for different imaging characteristics of ceT1 and hrT2 to perform structural boundary enhancement and ultimately generate high-quality pseudo-hrT2 images. Experiments validated on the CrossMoDA2022 dataset show that CE-UDA significantly improves the translation quality of pseudo hrT2 images in the ceT1 to hrT2 domain adaptation task, which in turn improves the segmentation performance of the downstream task.