PZSNet: a multiscale approach to address similar signal intensities and subtle textures in prostate MRI segmentation

Accurate segmentation of zones in prostate MRI is crucial for effective prostate cancer diagnosis and planning treatments. Computer-aided segmentation is important to overcome the complexity of subtle textural contrasts and similar signal intensities found between adjacent prostate zones, which include the Peripheral Zone (PZ) and Transition Zone (TZ), making it difficult to delineate the boundary. In this paper, we present PZSNet, which is a U-shaped hybrid transformer designed for accurate prostate zone segmentation. PZSNet leverages the multiscale feature learning and boundary-aware decoding to improve segmentation performance and to guarantee an accurate border delineation among various complicated prostate zones. The model introduces three key modules: (1) Adaptive Boundary Fusion Module (ABFM) which captures spatial dependencies and recalibrates channel wise importance to handle subtle textural differences (2) Dynamic Features Injection Module (DFIM) that enhances the multiscale context for better segmentation of zones with close signal intensities like AFS and TZ while preserving spatial resolution, and (3) Feature Enhancement Module (FEM), which adaptively enhances low level and high level features to ensure effective integration of fine details and broader context. Evaluated on the ProstateX and MSD datasets, PZSNet obtains the DSC of 96.97% for the PZ and 89.61% for the TZ, with HD95 values of 3.71 mm and 5.43 mm, respectively. These results indicated that PZSNet provides better segmentation performance, and has potential applications in clinical for prostate zone segmentation and assistive real-time cancer diagnosing and treatment planning.