Multi-zone Computer-aided Prostate Segmentation on MR Images Using a Deep Learning-based Approach
Multi-zone Computer-aided Prostate Segmentation on MR Images Using a Deep Learning-based Approach
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.
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Olivier.rouviere@chu-lyon.fr472 11 61 67 ext. +33