Artificial intelligence for brain neuroanatomical segmentation in magnetic resonance imaging: A literature review
M. D. Andrews, Antonio Di Ieva
Abstract
PURPOSE: This literature review aims to synthesise current research on the application of artificial intelligence (AI) for the segmentation of brain neuroanatomical structures in magnetic resonance imaging (MRI). METHODS: A literature search was conducted using the databases Embase, Medline, Scopus, and Web of Science, and captured articles were assessed for inclusion in the review. Data extraction was performed for the summary of the AI model used, and key findings of each article, advantages and disadvantages were identified. RESULTS: Following full-text screening, 21 articles were included in the review. The review covers models for segmentation models applied to the whole brain, cerebral cortex, subcortical structures, the cerebellum, blood vessels, perivascular spaces, and the ventricles. Accuracy of segmentation was generally high, particularly for segmenting neuroanatomical structures in healthy cohorts. CONCLUSION: The use of AI for automatic brain segmentation is generally highly accurate and fast for all regions of the human brain. Challenges include robustness to anatomical variability and pathology, largely due to difficulties with accessing sufficient training data.