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Deep Learning for Prostate Segmentation

NCT04191980 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 62

Last updated 2019-12-10

No results posted yet for this study

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

Interventions

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

Sponsors & Collaborators

  • Hospices Civils de Lyon

    lead OTHER

Eligibility

Min Age
18 Years
Sex
MALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2019-02-01
Primary Completion
2020-01-31
Completion
2020-06-30

Countries

  • France

Study Locations

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Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04191980 on ClinicalTrials.gov