Structure-Enhanced Unsupervised Domain Adaptation for CT Whole-Brain Segmentation

Abstract

Early and accurate identification of intracranial hemorrhage (ICH) is crucial for treatment, but the inherently low-contrast resolution of computed tomography (CT) imaging poses challenges in identification of specific cerebral regions, impacting effective and timely clinical decision-making. We propose brain structure-enhanced domain adaptation (BraSEDA), a CT-based unsupervised domain adaptation (UDA) model designed to assist in the identification of brain regions. BraSEDA framework utilizes a cross-modal instance normalization (CMIN) module for enhancing CT image structural features and creating high-quality pseudo magnetic resonance (MR) images. A multilevel CMIN architecture is also introduced for further improvement. The BraSEDA framework improved the quality of pseudo MR images in head CT to MR domain adaptation task, as reflected by the lowest-Fréchet inception distance scores $95.0\pm 12.1$ (p-value < 0.001) with and highest-BC scores $0.915\pm 0.396$ (p-value <0.01),>https://github.com/YixinChen-AI/BraSEDA .