An Algorithm Creation by Automated Segmentation by MRI for Bone and Muscles of Shoulder

Assistance Publique - Hôpitaux de Paris

Overview

The primary objective of the study is to develop an algorithm of automated segmentation of shoulder by MRI examinations.

This is a national monocentric study which will be conducted in Ambroise Paré hospital of APHP, in orthopaedics department (for enrollment) and radiological department (for CT-scan and MRI examinations) respectively. Manual segmentations of 5 muscles and 2 bones of shoulder by MRI with automated segmentation of shoulders corresponding to CT-scan imagings. 3D imagings of each shoulder by manual segmentations from MRI and automated segmentation from computed tomography will provide to build a network. The perspective of the elaborated algorithm should lead to an automated 3D-reconstruction of patients' shoulder as a routine care in surgery.

Study Type

INTERVENTIONAL

Allocation

Purpose

OTHER

Masking

NONE

Conditions

Reverse Shoulder Prosthesis

Interventions

CT-Scan and MRIPROCEDURE

CT-Scan and MRI examination for shoulder will be performed on healthy volunteers.

Eligibility

Sex: ALLMin age: 18 YearsHealthy volunteers:

Medical Language ↔ Plain English

Inclusion Criteria: * Healthy volunteer \> 18 years, presenting any symptom nor history of shoulder pathology; * Affiliated to social security scheme. Exclusion Criteria: * Symptoms or history of shoulder pathologies; * Claustrophobia; * Pregnant woman; * Patient covered by AME system; * Contre-indication to perform MRI examination (implant, less 6-months stent implantation, recent surgery, renal insufficiency, pace maker implantation, cardiac defibrillator, cardiovascular catheter, neurostimulation, implantable electronic pompe for automatic injection of medications).

Outcomes

Primary Outcomes

Algorithm developement

The developement for automatic segmentation algorithm: uses method with a convolutional neural networks (convolutional neural network - CNN).

Time frame: through study completion, an average of 8 month

Data from ClinicalTrials.gov

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