Clinical trial · Observational
Deep Learning for Prostate Segmentation
Multi-zone Computer-aided Prostate Segmentation on MR Images Using a Deep Learning-based Approach
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Prostate Cancer | Malignant Prostate Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Comparison of prostate multi-zone segmentation obtained with an automatic deep learning-based algorithm and two expert radiologists | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- Patients with a MRI on a 3 Tesla (T) unit
- description
- 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
- interventionNames
- Other: Comparison of prostate multi-zone segmentation obtained with an automatic deep learning-based algorithm and two expert radiologists
- label
- Patients with a MRI on a 1.5 Tesla unit
- description
- 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
- interventionNames
- Other: Comparison of prostate multi-zone segmentation obtained with an automatic deep learning-based algorithm and two expert radiologists
Primary outcomes (1)
- measure
- Mean Mesh Distance (Mean) between the contours of the whole prostate made by the algorithm and the two radiologists
Eligibility
Eligibility (as posted)
- Sex
- Male
- Minimum age
- 18 Years
Show eligibility criteria text
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
References
Publications (0)
Data not yet available