GOPI-Segm: Deep Learning for Prostate Segmentation

Sponsor

Hospices Civils de Lyon (Other)

Overall Status

Unknown status

CT.gov ID

NCT04191980

Collaborator

(none)

Enrollment

Location

Anticipated Duration (Months)

3.9

Patients Per Site Per Month

Study Details

Study Description

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.

Condition or Disease	Intervention/Treatment	Phase
Prostate Cancer	Other: Comparison of prostate multi-zone segmentation obtained with an automatic deep learning-based algorithm and two expert radiologists

Study Design

Study Type:

Observational

Anticipated Enrollment :

62 participants

Observational Model:

Cohort

Time Perspective:

Retrospective

Official Title:

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

Actual Study Start Date :

Feb 1, 2019

Anticipated Primary Completion Date :

Jan 1, 2020

Anticipated Study Completion Date :

Jun 1, 2020

Arms and Interventions

Arm	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

Arm

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

Outcome Measures

Primary Outcome Measures

Mean Mesh Distance (Mean) between the contours of the whole prostate made by the algorithm and the two radiologists [Month 11]
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.

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years and Older

Sexes Eligible for Study:

Male

Accepts Healthy Volunteers:

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

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Hôpital Edouard Herriot	Lyon		France	69008

Sponsors and Collaborators

Hospices Civils de Lyon

Investigators

None specified.

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

Responsible Party:

Hospices Civils de Lyon

ClinicalTrials.gov Identifier:

NCT04191980

Other Study ID Numbers:

GOPI-Segmentation_2019

First Posted:

Dec 10, 2019

Last Update Posted:

Dec 10, 2019

Last Verified:

Dec 1, 2019

Keywords provided by Hospices Civils de Lyon

prostate segmentation

MR images

Additional relevant MeSH terms:

Prostatic Neoplasms

Study Results

No Results Posted as of Dec 10, 2019