Deep Learning for Prostate Segmentation

Prostate Cancer Clinical Trial

— GOPI-Segm  

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04191980
• Other study ID #: GOPI-Segmentation_2019
• Status: Recruiting
• Start date: February 1, 2019
• Est. completion date: June 2020

Study information

• Verified date: December 2019
• Source: Hospices Civils de Lyon
• Contact: Olivier ROUVIERE, Pr
• Phone: 472 11 61 67
• Email: Olivier.rouviere[@]chu-lyon.fr
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 62
• Est. completion date: June 2020
• Est. primary completion date: January 2020
• Accepts healthy volunteers: No
• Gender: Male
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Prostate Cancer
• Prostatic Neoplasms

Intervention

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

Locations

Country	Name	City	State
France	Hôpital Edouard Herriot	Lyon

Sponsors (1)

Lead Sponsor	Collaborator
Hospices Civils de Lyon

Country where clinical trial is conducted

France, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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