Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging

• Published in: Anatomical science international 2023-07, Vol.98 (3), p.318-336
• Main Authors: Andica, Christina, Kamagata, Koji, Aoki, Shigeki
• Format: Article
• Language: English
• Subjects: Anatomy; Animal Anatomy; Animal Physiology; Brain - diagnostic imaging; Cell Biology; Diffusion Magnetic Resonance Imaging; Diffusion Tensor Imaging - methods; Histology; Human Physiology; Humans; Image processing; Image Processing, Computer-Assisted - methods; Magnetic resonance imaging; Medicine; Medicine & Public Health; Morphology; Neuroimaging; Neurosciences; Reproducibility of Results; Review; Review Article; Segmentation; Substantia alba; White Matter - anatomy & histology; White Matter - diagnostic imaging
• ISSN: 1447-6959; 1447-073X
• DOI: 10.1007/s12565-023-00715-9

White matter bundle segmentation using diffusion magnetic resonance imaging fiber tractography enables detailed evaluation of individual white matter tracts three-dimensionally, and plays a crucial role in studying human brain anatomy, function, development, and diseases. Manual extraction of streamlines utilizing a combination of the inclusion and exclusion of regions of interest can be considered the current gold standard for extracting white matter bundles from whole-brain tractograms. However, this is a time-consuming and operator-dependent process with limited reproducibility. Several automated approaches using different strategies to reconstruct the white matter tracts have been proposed to address the issues of time, labor, and reproducibility. In this review, we discuss few of the most well-validated approaches that automate white matter bundle segmentation with an end-to-end pipeline, including TRActs Constrained by UnderLying Anatomy (TRACULA), Automated Fiber Quantification, and TractSeg.