Preliminary Study on Stromal Nodule Detection Using B-Mode Ultrasound Images and Deep Learning

Benign prostatic hyperplasia (BPH) is a common condition among aging men. Currently, transrectal ultrasonography (TRUS) is used for detecting BPH but frequently falls short in accuracy. In this study, we investigated the use of a deep learning network for stromal nodule detection in prostate specimens exhibiting BPH using B-mode ultrasound (US) imaging. B-mode US data was captured from ex-vivo prostate specimens of human patients. Expert annotations differentiating between stromal and non-stromal regions for each prostate specimen were correlated with corresponding B-mode data and used as the reference for validation. The Segment Anything Model 2 (SAM2) network was trained and applied to segment stromal versus non-stromal tissue within B-mode ultrasound images. An 80/20 patient-level train/test split was used to evaluate model performance. Preliminary results demonstrate the feasibility of using SAM2 to identify stromal nodules directly from B-mode ultrasound data, achieving a Dice score of 0.76. Ongoing work focuses on increasing the dataset and improving segmentation accuracy. The study provides an approach to validate the capability of ultrasound imaging for BPH detection. More image data are needed to improve the segmentation performance.