Spots Global Cancer Trial Database for Deep Learning for Prostate Segmentation

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Trial Identification

Brief Title: Deep Learning for Prostate Segmentation

Official Title: Multi-zone Computer-aided Prostate Segmentation on MR Images Using a Deep Learning-based Approach

Study ID: NCT04191980

Conditions

Prostate Cancer

Interventions

Comparison of prostate multi-zone segmentation obtained with an automatic deep learning-based algorithm and two expert radiologists

Study Description

Brief Summary: Because the diagnostic criteria for prostate cancer are different in the peripheral and the transition zone, prostate segmentation is needed for any computer-aided diagnosis system aimed at characterizing prostate lesions on magnetic resonance (MR) images. Manual segmentation is time consuming and may differ between radiologists with different expertise. We developed and trained a convolutional neural network algorithm for segmenting the whole prostate, the transition zone and the anterior fibromuscular stroma on T2-weighted images of 787 MRIs from an existing prospective radiological pathological correlation database containing prostate MRI of patients treated by prostatectomy between 2008 and 2014 (CLARA-P database). The purpose of this study is to validate this algorithm on an independent cohort of patients.

Detailed Description: