Development and Validation of an Algorithm for Segmentation of the Prostate and its Zones from Three-dimensional Transrectal Multiparametric Ultrasound Images.

ABSTRACT:

Background and objective

Multiparametric ultrasound (mpUS) is being investigated as an alternative to magnetic resonance imaging (MRI) for detection of prostate cancer (PC). Automated prostate segmentation facilitates workflows, and zonal segmentation can aid in PC diagnosis, accounting for differences in imaging characteristics and tumor incidence. Our aim was to develop a deep learning algorithm that can automatically segment the prostate and its zones on three-dimensional (3D) contrast-enhanced ultrasound (CEUS) and conventional brightness-mode (B-mode) images (NCT04605276).

Methods

A total of 259 3D mpUS images were collected from men with suspicion for PC in a prospective multicenter trial to develop a computer-aided diagnosis system for PC. Manual segmentation was perf

SUBMITTER: van den Kroonenberg DL 

PROVIDER: S-EPMC12002784 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature