A comparison of automated lesion segmentation approaches for chronic stroke T1‐weighted MRI data

• Published in: Human brain mapping Vol. 40; no. 16; pp. 4669 - 4685
• Main Authors: Ito, Kaori L., Kim, Hosung, Liew, Sook‐Lei
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.11.2019
• Subjects: Algorithms; Automation; Bayes Theorem; Bayesian analysis; big data; Brain - diagnostic imaging; Brain stem; Cerebellum; Chronic Disease; chronic stroke; Cluster Analysis; Data analysis; Datasets; False Negative Reactions; Functional anatomy; Humans; Image Interpretation, Computer-Assisted; Image processing; Image Processing, Computer-Assisted; Image segmentation; lesion segmentation; Lesions; Literature reviews; Magnetic Resonance Imaging - methods; Medical imaging; MRI; Neuroimaging; Neurology; Normal Distribution; Rehabilitation; Stroke; Stroke - diagnostic imaging; Structure-function relationships; Territory; lesion segmentation; chronic stroke; big data; MRI; stroke

Accurate stroke lesion segmentation is a critical step in the neuroimaging processing pipeline for assessing the relationship between poststroke brain structure, function, and behavior. Many multimodal segmentation algorithms have been developed for acute stroke neuroimaging, yet few algorithms are...