Scalable and Transferable U-Net for Accurate Simultaneous 3D MRI Segmentation of Gestational Sac and Decidual Tissue in Cesarean Scar Pregnancy

Motivation: Cesarean scar pregnancies (CSP) pose significant risks and complications. Accurate segmentation of the gestational sac (GS) and decidual tissue (DEC) in CSP through MRI is crucial for diagnosis, but current methods are limited in effectiveness. Goal(s): Introduce a large-scale and pre-trained model, Scalable and Transferable U-Net (STU-Net), to accurately segment GS and DEC simultaneously. Approach: 151 CSP females with structural MRI were enrolled. STU-Net was trained and evaluated. Results: The proposed STU-Net achieved promising segmentation performance. Impact: The proposed STU-Net enables precise segmentations of GS and DEC, potentially enhancing the diagnostic accuracy of CSP.