Automatic Segmentation of Polycystic Liver

Study Overview
=================
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.

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

Conditions
-----------------
Polycystic Liver Disease, Polycystic Hepatorenal Disease, Liver Injury

Intervention / Treatment
-----------------
* Other: Anonymized CT examinations
* Other: Training (1)
* Other: Training (2)
* Other: Validation (1)
* Other: Validation (2)


Participation Criteria
=================
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

Ages Eligible for Study
-----------------
Minimum Age: 18 Years

Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
No

Study Plan
=================
How is the study designed?
-----------------

Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Neuronal network Training group<br>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). | Other: Anonymized CT examinations<br>* 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.<br>Other: Training (1)<br>* 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<br>Other: Training (2)<br>* An initial training phase of the artificial intelligence network will be carried out : - Use of computer data to drive the artificial intelligence network.<br>|
| Neuronal network Validation group<br>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). | Other: Anonymized CT examinations<br>* 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.<br>Other: Validation (1)<br>* 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<br>Other: Validation (2)<br>* 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<br>|

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 | 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) Manual segmentation of polycystic livers of the 1st training group and deep learning of convolutional neural network Manual segmentation of polycystic livers of 2nd validation group Test of automatic segmentation by the convolutional neural network on these group and collection of data set | At 4 months after randomization |