Weakly supervised deep learning for multimodal MRI-TRUS registration: Toward assisting prostate biopsy guidance.

ABSTRACT:

Objective

Accurate magnetic resonance imaging-transrectal ultrasound (MRI-TRUS) registration is essential for improving prostate cancer detection and biopsy guidance. This study presents a weakly supervised learning framework to enhance image alignment while minimizing reliance on extensive labeled data.

Methods

We developed a two-stage weakly supervised framework integrating an attention-enhanced U-Net for prostate segmentation and a residual-enhanced registration network (RERN) for MRI-TRUS alignment. The segmentation model was trained and evaluated on public MRI datasets (MSD Prostate, Promise12, µ-RegPro) and TRUS datasets (µ-RegPro), with additional testing on a clinical cohort of 32 MRI-TRUS pairs. Performance was assessed using dice similarity coefficient (DSC), accur

SUBMITTER: Yu J 

PROVIDER: S-EPMC12417678 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature