A deep learning masked segmentation alternative to manual segmentation in biparametric MRI prostate cancer radiomics.

ABSTRACT:

Objectives

To determine the value of a deep learning masked (DLM) auto-fixed volume of interest (VOI) segmentation method as an alternative to manual segmentation for radiomics-based diagnosis of clinically significant (CS) prostate cancer (PCa) on biparametric magnetic resonance imaging (bpMRI).

Materials and methods

This study included a retrospective multi-center dataset of 524 PCa lesions (of which 204 are CS PCa) on bpMRI. All lesions were both semi-automatically segmented with a DLM auto-fixed VOI method (averaging < 10 s per lesion) and manually segmented by an expert uroradiologist (averaging 5 min per lesion). The DLM auto-fixed VOI method uses a spherical VOI (with its center at the location of the lowest apparent diffusion coefficient of the prostate lesion as ind

SUBMITTER: Bleker J 

PROVIDER: S-EPMC9381625 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature