Transformers for Neuroimage Segmentation: Scoping Review.

ABSTRACT:

Background

Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation.

Objective

This scoping review will synthesize current literature and assess the use of various transformer models for neuroimaging segmentation.

Methods

A systematic search in major databases, including Scopus, IEEE Xplore, PubMed, and ACM Digital Library, was carried out for studies applying transformers to neuroimaging segmentation problems from 2019 through 2023. The inclusion criteria allow only for peer-reviewed journal papers and conference papers foc

SUBMITTER: Iratni M 

PROVIDER: S-EPMC11822320 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature