ProSeg: multi-scale context fusion for high-precision prostate segmentation in MRI

Abstract Prostate MRI segmentation is critical for accurate diagnosis and treatment planning but remains challenging due to the complex interplay between the peripheral zone’s thin, irregular boundaries and the central gland’s homogeneous textures, compounded by variability across imaging protocols. To address these challenges, we propose ProSeg, a novel deep learning framework featuring a specialized ProSeg block that integrates dual complementary processes: (1) anisotropic convolutions for precise peripheral zone boundary delineation and (2) cross-slice attention mechanisms for robust central gland texture modeling. Extensive evaluations on the Promise12 and Promise158 datasets demonstrate ProSeg’s state-of-the-art performance, achieving Dice scores of 84.31% (peripheral zone) and 57.92% (central gland) on Promise12, and 83.15% (peripheral zone) and 56.38% (central gland) on Promise158, significantly outperforming existing methods. ProSeg’s consistent accuracy across diverse protocols highlights its clinical potential for reliable prostate zonal segmentation in real-world settings.