Deep Learning for Prostate Segmentation (GOPI-Segm)

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.

Study Overview

• Status: Unknown
• Conditions: Prostate Cancer
• Intervention / Treatment: Other: Comparison of prostate multi-zone segmentation obtained with an automatic deep learning-based algorithm and two expert radiologists

• Study Type: Observational
• Enrollment (Anticipated): 62

Contacts and Locations

Study Locations

• France
  • Lyon, France, 69008
    • Recruiting
    • Hôpital Edouard Herriot
    • Contact: Olivier ROUVIERE, Pr

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: Male

• Sampling Method: Non-Probability Sample

Study Population

Random selection in the Picture Archiving and Communication System (PACS) of the Hospices Civils de Lyon among examinations performed between 2016 and 2019

Description

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

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Retrospective

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Patients with a MRI on a 3 Tesla (T) unit; 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; 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
Patients with a MRI on a 1.5 Tesla unit; 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; 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

What is the study measuring?

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

Collaborators and Investigators

• Sponsor: Hospices Civils de Lyon

Study record dates

Study Major Dates

• Study Start (Actual): February 1, 2019
• Primary Completion (Anticipated): January 1, 2020
• Study Completion (Anticipated): June 1, 2020

Study Registration Dates

• First Submitted: December 6, 2019
• First Submitted That Met QC Criteria: December 6, 2019
• First Posted (Actual): December 10, 2019

Study Record Updates

• Last Update Posted (Actual): December 10, 2019
• Last Update Submitted That Met QC Criteria: December 6, 2019
• Last Verified: December 1, 2019

More Information

Terms related to this study

• Keywords: prostate segmentation; MR images
• Additional Relevant MeSH Terms: Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Genital Neoplasms, Male; Prostatic Diseases; Prostatic Neoplasms
• Other Study ID Numbers: GOPI-Segmentation_2019