Deep Learning for Prostate Segmentation

Study Overview
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Brief Summary
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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.

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

Conditions
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Prostate Cancer

Intervention / Treatment
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* Other: Comparison of prostate multi-zone segmentation obtained with an automatic deep learning-based algorithm and two expert radiologists


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Prostate MRI contained in the PACS of the Hospices Civils de Lyon Performed in 2016-2019 Exclusion Criteria: MRIs from patients who already had treatment for prostate cancer

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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Male

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Patients with a MRI on a 3 Tesla (T) unit<br>The total validation cohort is composed of axial T2-weighted images of the prostate obtained from 31 prostate MRIs on a 3T unit randomly chosen among the prostate MRIs performed at the Hospices Civils de Lyon in 20162015-2019 | Other: Comparison of prostate multi-zone segmentation obtained with an automatic deep learning-based algorithm and two expert radiologists<br>* The algorithm is used to perform a multizone segmentation of the prostate including delineation of : the whole prostate contours, the transition zone contours, the anterior fibromuscular stroma. The contours is independently corrected by 2 radiologists. The corrected contours of the different zones will be stored and for each zone 6 different metrics will be used to evaluate the difference between the initial and corrected contours: Mean Mesh Distance: Average Boundary Distance (ABD) for each point of the reference segmentation. The distance to the closest point of the compared segmentation is first computed. Then the average of all these distances is computed and gives the ABD General Hausdorff distance (HD) 95% percentile (P) of the HD and the 95th (P) of the asymmetric HD distribution 95% HD modified (HD95_1): different approach by first computing the 95th (P) of the asymmetric HD then taking the maximum Dice coefficient Difference in volumes<br>|
| Patients with a MRI on a 1.5 Tesla unit<br>The total validation cohort is composed of axial T2-weighted images of the prostate obtained from 31 prostate MRIs on a 1.5T unit randomly chosen among the prostate MRIs performed at the Hospices Civils de Lyon in 20162015-2019 | Other: Comparison of prostate multi-zone segmentation obtained with an automatic deep learning-based algorithm and two expert radiologists<br>* The algorithm is used to perform a multizone segmentation of the prostate including delineation of : the whole prostate contours, the transition zone contours, the anterior fibromuscular stroma. The contours is independently corrected by 2 radiologists. The corrected contours of the different zones will be stored and for each zone 6 different metrics will be used to evaluate the difference between the initial and corrected contours: Mean Mesh Distance: Average Boundary Distance (ABD) for each point of the reference segmentation. The distance to the closest point of the compared segmentation is first computed. Then the average of all these distances is computed and gives the ABD General Hausdorff distance (HD) 95% percentile (P) of the HD and the 95th (P) of the asymmetric HD distribution 95% HD modified (HD95_1): different approach by first computing the 95th (P) of the asymmetric HD then taking the maximum Dice coefficient Difference in volumes<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Mean Mesh Distance (Mean) between the contours of the whole prostate made by the algorithm and the two radiologists | The Mean Mesh Distance corresponds to the Average Boundary Distance (ABD) for each point of the reference segmentation. The distance to the closest point of the compared segmentation is first computed. Then the average of all these distances is computed and gives the ABD. The Mean Mesh Distance between the contours of the whole prostate made by the algorithm and each radiologist will be used as primary outcome measure. | Month 11 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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prostate segmentation, MR images