Deep Learning for Prostate and Central Gland Segmentation on Micro-Ultrasound Images

Prostate cancer ranks as the second most prevalent cancer among men globally. Accurate segmentation of prostate and the central gland plays a pivotal role in detecting abnormalities within the prostate, paving the way for early detection of prostate cancer, quantitative analysis and subsequent treatment planning. Micro-ultrasound (MUS) imaging is a novel ultrasound technique that operates at frequencies above 20 MHz and offers superior resolution compared to conventional ultrasound, making it particularly effective for visualizing fine anatomical structures and pathological changes. In this paper, we leverage deep learning (DL) techniques for the segmentation of prostate and its central gland on micro-ultrasound images, investigating their potential in prostate cancer detection. We trained our DL model on MUS images from 80 patients, utilizing a fivefold cross-validation. We achieved Dice similarity coefficient (DSC) scores of 0.918 and 0.833, and an average surface-to-surface distance (SSD) of 1.176 mm and 1.795 mm for the prostate and the central gland, respectively. We futher evaluated our method on a publicly available MUS dataset, achieving a DSC score of 0.957 and a Hausdorff Distance (HD) of 1.922 mm for prostate segmentation. These results outperform the current state-of-the-art (SOTA).