Unified MRI Brain Image Translation via Hierarchical Tumor Structure Comparison
Researchers have developed a new generative adversarial model called HTSCGAN for multi-modal MRI brain image translation. This model integrates hierarchical tumor structure information to improve the quality and clinical applicability of translated images. The generator uses a Patch Contrast Module with varying patch sizes to capture structural details, while a pretrained Patch Classifier and Structure-Aware Encoder ensure the generated images retain the ground truth tumor structure. Experiments on BraTS2020 and BraTS2021 datasets show HTSCGAN performs well in both translation and downstream segmentation tasks. AI
IMPACT This research could lead to more accurate medical diagnoses and treatment planning through improved MRI image translation.