MRI/TRUS-Guided Auxiliary System for Transperineal Prostate Biopsy Based on Deep Learning

Prostate cancer is prevalent cancer worldwide, ranking fourth in frequency. A systematic biopsy can significantly increase a patient's 5-year survival rate. However, accurate and effective biopsy based on pre-biopsy magnetic resonance imaging (MRI) and hand-held transrectal ultrasound (TRUS) is reliant on the operator's expertise. This paper presents a novel MRI/TRUS auxiliary system based on the deep learning method for prostate intervention, including the system architecture and workflow. The system comprises a preprocessing unit, a registration unit, and a navigation unit. The nnU-net is utilized to generate segmentation labels for the prostate and surrounding organs. The DeepReg network is used in the registration unit to fuse real-time TRUS and pre-biopsy MRI. The navigation unit provides reference insertion positions and alerts the operator of potential collisions and damage to nearby organs. The experimental results, based on data from both the TCIA and hospitals, demonstrate the effectiveness and feasibility of the auxiliary system.