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21 December 2018 Robust automatic corpus callosum analysis toolkit: mapping callosal development across heterogeneous multisite data
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Proceedings Volume 10975, 14th International Symposium on Medical Information Processing and Analysis; 109750M (2018) https://doi.org/10.1117/12.2506661
Event: 14th International Symposium on Medical Information Processing and Analysis, 2018, Mazatlán, Mexico
Abstract
The corpus callosum (CC) is the main neural pathway that communicates information between the brain’s hemispheres. Impairment of this pathway is evident in neurogenetic and developmental disorders, neurodegenerative diseases, and in many major psychiatric disorders, making the CC the focus of intense study. Prior studies often require manual input for segmentation, or have been single site, single modality, or fail to report on the reliability and generalizability of segmentations. We develop a Robust Automatic Corpus Callosum Analysis Toolkit (RACCAT) that segments the midsagittal CC from T1-weighted images, guided by diffusion MRI where available, to facilitate large-scale multimodal CC studies of its global, regional, and local (pointwise) structure. RACCAT was applied to data from 772 individuals aged 3-21 from the Pediatric Imaging, Neurocognition, and Genetics study, a developmental cohort imaged using multiple scanners and imaging protocols. CC area and fractional anisotropy were associated with age but also with site and scanner manufacturer; CC curvature also showed significant age associations but showed no detectable association with the scanning site, making it a robust developmental biomarker for multisite studies.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alyssa H. Zhu, Arvin Saremi, Armand Amini, Ricardo Pires, Paul M. Thompson, and Neda Jahanshad "Robust automatic corpus callosum analysis toolkit: mapping callosal development across heterogeneous multisite data", Proc. SPIE 10975, 14th International Symposium on Medical Information Processing and Analysis, 109750M (21 December 2018); https://doi.org/10.1117/12.2506661
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