Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis.

ABSTRACT:

Objectives

Different machine learning algorithms (MLAs) for automated segmentation of gliomas have been reported in the literature. Automated segmentation of different tumor characteristics can be of added value for the diagnostic work-up and treatment planning. The purpose of this study was to provide an overview and meta-analysis of different MLA methods.

Methods

A systematic literature review and meta-analysis was performed on the eligible studies describing the segmentation of gliomas. Meta-analysis of the performance was conducted on the reported dice similarity coefficient (DSC) score of both the aggregated results as two subgroups (i.e., high-grade and low-grade gliomas). This study was registered in PROSPERO prior to initiation (CRD42020191033).

Results

After

SUBMITTER: van Kempen EJ 

PROVIDER: S-EPMC8589805 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature