Automatic Segmentation of Polycystic Liver

Brief Title

Automatic Segmentation of Polycystic Liver

Official Title

Automatic Segmentation by a Convolutional Neural Network (Artificial Intelligence - Deep Learning) of Polycystic Livers, as a Model of Multi-lesional Dysmorphic Livers

Brief Summary

      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 Type

Observational

Primary Outcome

Test of automatic segmentation by the convolutional neural network on these group and collection of data set

Condition

Polycystic Liver Disease

Intervention

Anonymized CT examinations

Study Arms / Comparison Groups

 Neuronal network Training group

Description:  The following radiological variables, related to each CT examinations, will be collected for each patient:
Injection modalities (without injection, injected)
Major hepatectomy surgery
Importance of hepatic dysmorphia
Presence of intraperitoneal fluid effusion
Presence of renal polycystosis (especially on the right side).

Publications

* Includes publications given by the data provider as well as publications identified by National Clinical Trials Identifier (NCT ID) in Medline.

Recruitment Status

Other

Estimated Enrollment

120

Start Date

April 1, 2019

Completion Date

September 2019

Primary Completion Date

July 2019

Eligibility Criteria

        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
      

Gender

All

Ages

18 Years - N/A

Accepts Healthy Volunteers

No

Contacts

, 472110400, [email protected]

Location Countries

France

Location Countries

France

NCT ID

NCT03960710

Organization ID

ASEPOL

Responsible Party

Sponsor

Study Sponsor

Hospices Civils de Lyon

Study Sponsor

, , 

Verification Date

May 2019