Deep Learning for Prostate Segmentation

NCT ID: NCT04191980

Last Updated: 2019-12-10

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 62 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2019-02-01
• Study Completion Date: 2020-06-30

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.

Conditions

Prostate Cancer

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

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

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

Intervention Type: OTHER

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

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

Intervention Type: OTHER

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

Interventions

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

Intervention Type: OTHER

Eligibility Criteria

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: MALE
• Accepts Healthy Volunteers: No

Sponsors

Hospices Civils de Lyon

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Hôpital Edouard Herriot

Lyon, , France

Site Status: RECRUITING

Countries

France

Central Contacts

Olivier ROUVIERE, Pr

Role: CONTACT

Olivier.rouviere@chu-lyon.fr

472 11 61 67 ext. +33

Facility Contacts

Olivier ROUVIERE, Pr

Role: primary

Other Identifiers

GOPI-Segmentation_2019

Identifier Type: -

Identifier Source: org_study_id