Automatic Segmentation of Polycystic Liver — ASEPOL  

Liver Injury Clinical Trial

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

Status Recruiting

Clinical Trial 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.

Clinical Trial Description

n/a

Related Conditions & MeSH terms

• Cysts
• Liver Diseases
• Liver Injury
• Polycystic Hepatorenal Disease
• Polycystic Liver Disease

• NCT number: NCT03960710
• Study type: Observational
• Source: Hospices Civils de Lyon
• Contact: Bénédicte CAYOT
• Phone: 472110400
• Email: benedicte.cayot@chu-lyon.fr
• Status: Recruiting
• Start date: April 1, 2019
• Completion date: September 2019