Multi-stage semi-supervised learning enhances white matter hyperintensity segmentation.

ABSTRACT:

Introduction

White matter hyperintensities (WMHs) are frequently observed on magnetic resonance (MR) images in older adults, commonly appearing as areas of high signal intensity on fluid-attenuated inversion recovery (FLAIR) MR scans. Elevated WMH volumes are associated with a greater risk of dementia and stroke, even after accounting for vascular risk factors. Manual segmentation, while considered the ground truth, is both labor-intensive and time-consuming, limiting the generation of annotated WMH datasets. Un-annotated data are relatively available; however, the requirement of annotated data poses a challenge for developing supervised machine learning models.

Methods

To address this challenge, we implemented a multi-stage semi-supervised learning (M3SL) approach that firs

SUBMITTER: Duarte KTN 

PROVIDER: S-EPMC11534601 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature