Automatic Brain Lesion Segmentation on Standard Magnetic Resonance Images: a Scoping Review

Overview

Journal BMJ Open

Specialty General Medicine

Date 2021 Jan 30

PMID 33514580

Citations 10

Authors

Emilia Gryska

Justin Schneiderman

Isabella Bjorkman-Burtscher

Rolf A Heckemann

Affiliations

Soon will be listed here.

Abstract

Objectives: Medical image analysis practices face challenges that can potentially be addressed with algorithm-based segmentation tools. In this study, we map the field of automatic MR brain lesion segmentation to understand the clinical applicability of prevalent methods and study designs, as well as challenges and limitations in the field.

Design: Scoping review.

Setting: Three databases (PubMed, IEEE Xplore and Scopus) were searched with tailored queries. Studies were included based on predefined criteria. Emerging themes during consecutive title, abstract, methods and whole-text screening were identified. The full-text analysis focused on materials, preprocessing, performance evaluation and comparison.

Results: Out of 2990 unique articles identified through the search, 441 articles met the eligibility criteria, with an estimated growth rate of 10% per year. We present a general overview and trends in the field with regard to publication sources, segmentation principles used and types of lesions. Algorithms are predominantly evaluated by measuring the agreement of segmentation results with a trusted reference. Few articles describe measures of clinical validity.

Conclusions: The observed reporting practices leave room for improvement with a view to studying replication, method comparison and clinical applicability. To promote this improvement, we propose a list of recommendations for future studies in the field.

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