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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
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:
Minimum Age: 18 Years
Eligible Ages: ADULT, OLDER_ADULT
Sex: MALE
Healthy Volunteers: No
Hôpital Edouard Herriot, Lyon, , France