Convolutional neural network-based segmentation can help in assessing the substantia nigra in neuromelanin MRI.

ABSTRACT:

Purpose

This study aimed to evaluate the accuracy and diagnostic test performance of the U-net-based segmentation method in neuromelanin magnetic resonance imaging (NM-MRI) compared to the established manual segmentation method for Parkinson's disease (PD) diagnosis.

Methods

NM-MRI datasets from two different 3T-scanners were used: a "principal dataset" with 122 participants and an "external validation dataset" with 24 participants, including 62 and 12 PD patients, respectively. Two radiologists performed SNpc manual segmentation. Inter-reader precision was determined using Dice coefficients. The U-net was trained with manual segmentation as ground truth and Dice coefficients used to measure accuracy. Training and validation steps were performed on the principal dataset usin

SUBMITTER: Le Berre A 

PROVIDER: S-EPMC6848644 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature