Automated segmentation of magnetic resonance bone marrow signal: a feasibility study.

ABSTRACT:

Background

Manual assessment of bone marrow signal is time-consuming and requires meticulous standardisation to secure adequate precision of findings.

Objective

We examined the feasibility of using deep learning for automated segmentation of bone marrow signal in children and adolescents.

Materials and methods

We selected knee images from 95 whole-body MRI examinations of healthy individuals and of children with chronic non-bacterial osteomyelitis, ages 6-18 years, in a longitudinal prospective multi-centre study cohort. Bone marrow signal on T2-weighted Dixon water-only images was divided into three color-coded intensity-levels: 1 = slightly increased; 2 = mildly increased; 3 = moderately to highly increased, up to fluid-like signal. We trained a convolutional neural

SUBMITTER: von Brandis E 

PROVIDER: S-EPMC9107442 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature