Deep-Learning-Based Neural Tissue Segmentation of MRI in Multiple Sclerosis: Effect of Training Set Size.

ABSTRACT:

Background

The dependence of deep-learning (DL)-based segmentation accuracy of brain MRI on the training size is not known.

Purpose

To determine the required training size for a desired accuracy in brain MRI segmentation in multiple sclerosis (MS) using DL.

Study type

Retrospective analysis of MRI data acquired as part of a multicenter clinical trial.

Study population

In all, 1008 patients with clinically definite MS.

Field strength/sequence

MRIs were acquired at 1.5T and 3T scanners manufactured by GE, Philips, and Siemens with dual turbo spin echo, FLAIR, and T₁ -weighted turbo spin echo sequences.

Assessment

Segmentation results using an automated analysis pipeline and validated by two neuroimaging experts served as the ground truth.

SUBMITTER: Narayana PA 

PROVIDER: S-EPMC7165037 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature