Deep learning for prostate and central gland segmentation on micro-ultrasound images

Lichun Zhang; Steve Ran Zhou; MoonHyung Choi; Richard E. Fan; Shengtian Sang; Geoffrey A. Sonn; Mirabela Rusu

doi:10.1117/12.3008845

1 April 2024 Deep learning for prostate and central gland segmentation on micro-ultrasound images

Lichun Zhang, Steve Ran Zhou, MoonHyung Choi, Richard E. Fan, Shengtian Sang, Geoffrey A. Sonn, Mirabela Rusu

Author Affiliations +

Proceedings Volume 12932, Medical Imaging 2024: Ultrasonic Imaging and Tomography; 1293207 (2024) https://doi.org/10.1117/12.3008845
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

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 20MHz 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 five-fold cross-validation. We achieved Dice similarity coefficient (DSC) scores of 0.918 and 0.833, and an average surface-to-surface distance (SSD) of 1.176mm and 1.795mm for the prostate and the central gland, respectively. We further evaluated our method on a publicly available MUS dataset, achieving a DSC score of 0.957 and a Hausdorff Distance (HD) of 1.922mm for prostate segmentation. These results outperform the current state-of-the- art (SOTA).

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lichun Zhang, Steve Ran Zhou, MoonHyung Choi, Richard E. Fan, Shengtian Sang, Geoffrey A. Sonn, and Mirabela Rusu "Deep learning for prostate and central gland segmentation on micro-ultrasound images", Proc. SPIE 12932, Medical Imaging 2024: Ultrasonic Imaging and Tomography, 1293207 (1 April 2024); https://doi.org/10.1117/12.3008845

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