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Deep Learning for Prostate Segmentation

2019-12-13 01:51:53 | BioPortfolio

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.

Study Design

Conditions

Prostate Cancer

Intervention

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

Location

Hôpital Edouard Herriot
Lyon
France
69008

Status

Recruiting

Source

Hospices Civils de Lyon

Results (where available)

View Results

Links

Published on BioPortfolio: 2019-12-13T01:51:53-0500

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