Cortically constrained shape recognition: Automated white matter tract segmentation validated in the pediatric brain.

Autor:	Jordan KM; Department of Neurology, University of California, UCSF Memory and Aging Center, Sandler Neurosciences Center, San Francisco, California, USA.; Dyslexia Center, University of California, San Francisco, California, USA., Lauricella M; Department of Neurology, University of California, UCSF Memory and Aging Center, Sandler Neurosciences Center, San Francisco, California, USA.; Dyslexia Center, University of California, San Francisco, California, USA., Licata AE; Department of Neurology, University of California, UCSF Memory and Aging Center, Sandler Neurosciences Center, San Francisco, California, USA.; Dyslexia Center, University of California, San Francisco, California, USA., Sacco S; Weill Institute for Neurosciences, University of California, San Francisco, California, USA.; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy., Asteggiano C; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy., Wang C; Department of Neurology, University of California, UCSF Memory and Aging Center, Sandler Neurosciences Center, San Francisco, California, USA.; Dyslexia Center, University of California, San Francisco, California, USA., Sudarsan SP; Department of Neurology, University of California, UCSF Memory and Aging Center, Sandler Neurosciences Center, San Francisco, California, USA.; Dyslexia Center, University of California, San Francisco, California, USA., Watson C; Department of Neurology, University of California, UCSF Memory and Aging Center, Sandler Neurosciences Center, San Francisco, California, USA.; Dyslexia Center, University of California, San Francisco, California, USA., Scheffler AW; Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA., Battistella G; Department of Neurology, University of California, UCSF Memory and Aging Center, Sandler Neurosciences Center, San Francisco, California, USA.; Dyslexia Center, University of California, San Francisco, California, USA., Miller ZA; Department of Neurology, University of California, UCSF Memory and Aging Center, Sandler Neurosciences Center, San Francisco, California, USA.; Dyslexia Center, University of California, San Francisco, California, USA., Gorno-Tempini ML; Department of Neurology, University of California, UCSF Memory and Aging Center, Sandler Neurosciences Center, San Francisco, California, USA.; Dyslexia Center, University of California, San Francisco, California, USA.; Department of Psychiatry, University of California, San Francisco, California, USA., Caverzasi E; Dyslexia Center, University of California, San Francisco, California, USA.; Weill Institute for Neurosciences, University of California, San Francisco, California, USA., Mandelli ML; Department of Neurology, University of California, UCSF Memory and Aging Center, Sandler Neurosciences Center, San Francisco, California, USA.; Dyslexia Center, University of California, San Francisco, California, USA.
Jazyk:	angličtina
Zdroj:	Journal of neuroimaging : official journal of the American Society of Neuroimaging [J Neuroimaging] 2021 Jul; Vol. 31 (4), pp. 758-772. Date of Electronic Publication: 2021 Apr 20.
DOI:	10.1111/jon.12854
Abstrakt:	Background and Purpose: Manual segmentation of white matter (WM) bundles requires extensive training and is prohibitively labor-intensive for large-scale studies. Automated segmentation methods are necessary in order to eliminate operator dependency and to enable reproducible studies. Significant changes in the WM landscape throughout childhood require flexible methods to capture the variance across the span of brain development. Methods: Here, we describe a novel automated segmentation tool called Cortically Constrained Shape Recognition (CCSR), which combines two complementary approaches: (1) anatomical connectivity priors based on FreeSurfer-derived regions of interest and (2) shape priors based on 3-dimensional streamline bundle atlases applied using RecoBundles. We tested the performance and repeatability of this approach by comparing volume and diffusion metrics of the main language WM tracts that were both manually and automatically segmented in a pediatric cohort acquired at the UCSF Dyslexia Center (n = 59; 25 females; average age: 11 ± 2; range: 7-14). Results: The CCSR approach showed high agreement with the expert manual segmentations: across all tracts, the spatial overlap between tract volumes showed an average Dice Similarity Coefficient (DSC) of 0.76, and the fractional anisotropy (FA) on average had a Lin's Concordance Correlation Coefficient (CCC) of 0.81. The CCSR's repeatability in a subset of this cohort achieved a DSC of 0.92 on average across all tracts. Conclusion: This novel automated segmentation approach is a promising tool for reproducible large-scale tractography analyses in pediatric populations and particularly for the quantitative assessment of structural connections underlying various clinical presentations in neurodevelopmental disorders. (© 2021 American Society of Neuroimaging.)
Databáze:	MEDLINE
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