- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT03960710
Automatic Segmentation of Polycystic Liver (ASEPOL)
Automatic Segmentation by a Convolutional Neural Network (Artificial Intelligence - Deep Learning) of Polycystic Livers, as a Model of Multi-lesional Dysmorphic Livers
Assessing the volume of the liver before surgery, predicting the volume of liver remaining after surgery, detecting primary or secondary lesions in the liver parenchyma are common applications that require optimal detection of liver contours, and therefore liver segmentation.
Several manual and laborious, semi-automatic and even automatic techniques exist.
However, severe pathology deforming the contours of the liver (multi-metastatic livers...), the hepatic environment of similar density to the liver or lesions, the CT examination technique are all variables that make it difficult to detect the contours. Current techniques, even automatic ones, are limited in this type of case (not rare) and most often require readjustments that make automatisation lose its value.
All these criteria of segmentation difficulties are gathered in the livers of hepatorenal polycystosis, which therefore constitute an adapted study model for the development of an automatic segmentation tool.
To obtain an automatic segmentation of any lesional liver, by exceeding the criteria of difficulty considered, investigators have developed a convolutional neural network (artificial intelligence - deep learning) useful for clinical practice.
Study Overview
Status
Intervention / Treatment
Study Type
Enrollment (Anticipated)
Contacts and Locations
Study Contact
- Name: Bénédicte CAYOT
- Phone Number: +33 472110400
- Email: benedicte.cayot@chu-lyon.fr
Study Contact Backup
- Name: Pierre-Jean VALETTE, MD, Prof.
- Phone Number: +33 472117544
- Email: pierre-jean.valette@chu-lyon.fr
Study Locations
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-
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Lyon, France, 69437
- Recruiting
- Service de radiologie - Pavillon B - Cellule Recherche imagerie, Hôpital Edouard Herriot (HCL)
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Contact:
- Bénédicte CAYOT
- Phone Number: +33 472110400
- Email: benedicte.cayot@chu-lyon.fr
-
Contact:
- Pierre-Jean VALETTE, MD, Prof.
- Phone Number: +33 472117544
- Email: pierre-jean.valette@chu-lyon.fr
-
Principal Investigator:
- Pierre-Jean VALETTE, MD, Prof.
-
-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- Patients ≥ 18 years old
- Patients with hepato-renal polycystosis, with or without surgery
- Patients with at least one abdominal-pelvic CT scan without injection or with injection between January 1, 2016 and August 2018
- Patients with good quality and available images
Exclusion Criteria:
- Patients with no CT scan images available
- Patients with bad quality of CT scan images
Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
---|---|
Neuronal network Training group
The following radiological variables, related to each CT examinations, will be collected for each patient:
|
The anonymized CT examinations will be reviewed in Lyon, in the imaging department of Edouard Herriot Hospital, by an expert radiologist and an intern from the Lyon hospitals.
An initial training phase of the artificial intelligence network will be carried out : - Segmentation of the livers of a first part of the CT examination, by an intern of the Lyon hospitals An initial training phase of the artificial intelligence network will be carried out : - Use of computer data to drive the artificial intelligence network. |
Neuronal network Validation group
The following radiological variables, related to each CT examinations, will be collected for each patient:
|
The anonymized CT examinations will be reviewed in Lyon, in the imaging department of Edouard Herriot Hospital, by an expert radiologist and an intern from the Lyon hospitals.
A validation phase of the artificial intelligence tool will be carried out with segmentation of the livers of the second part of the CT examinations : - Carried out by an intern at the Lyon hospitals A validation phase of the artificial intelligence tool will be carried out with segmentation of the livers of the second part of the CT examinations : - Carried out by the neural network |
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Test of automatic segmentation by the convolutional neural network on these group and collection of data set
Time Frame: At 4 months after randomization
|
Development of an automatic segmentation tool for highly dysmorphic polycystic livers as a prerequisite for segmentation of any type of multi-lesional livers that are difficult to segment, in order to facilitate lesion detection and volume measurement in clinical practice. Randomisation of the patient into two data groups, one for training the other for Validating the convolutional neural network (artificial intelligence)
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At 4 months after randomization
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Collaborators and Investigators
Sponsor
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Anticipated)
Study Completion (Anticipated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- ASEPOL
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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