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Proceedings Paper

Automatic measurement of extra-axial CSF from infant MRI data
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Paper Abstract

The quantification of cerebrospinal fluid (CSF) in the human brain has shown to play an important role in early postnatal brain development. Extra-axial fluid (EA-CSF), which is characterized by CSF in the subarachnoid space, is a promising marker for the early detection of children at risk for neurodevelopmental disorders, such as Autism Spectrum Disorder (ASD). Yet, non-ventricular CSF quantification, in particular extra-axial CSF quantification, is not supported in the major neuro-imaging software solutions, such as FreeSurfer. Most current structural image analysis packages mask out the extra-axial CSF space in one of the first pre-processing steps. A quantitative protocol was previously developed by our group to objectively measure the volume of total EA-CSF volume using a pipeline workflow implemented in a series of python scripts. While this solution worked for our specific lab, a graphical user interface-based tool is necessary to facilitate the computation of extra-axial CSF volume across a wide array of neuroimaging studies and research labs. This paper presents the development of a novel open-source, cross-platform, user-friendly software tool, called Auto-EACSF, for the automatic computation of such extra-axial CSF volume. Auto-EACSF allows neuroimaging labs to quantify extra-axial CSF in their neuroimaging studies in order to investigate its role in normal and atypical brain development.

Paper Details

Date Published: 28 February 2020
PDF: 6 pages
Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 113171I (28 February 2020); doi: 10.1117/12.2550006
Show Author Affiliations
Arthur LeMaout, The Univ. of North Carolina at Chapel Hill (United States)
Han Bit Yoon, The Univ. of North Carolina at Chapel Hill (United States)
Sun Hyung Kim, The Univ. of North Carolina at Chapel Hill (United States)
Mahmoud Mostapha, The Univ. of North Carolina at Chapel Hill (United States)
Mark D. Shen, The Univ. of North Carolina at Chapel Hill (United States)
Juan Prieto, The Univ. of North Carolina at Chapel Hill (United States)
Martin Styner, The Univ. of North Carolina at Chapel Hill (United States)


Published in SPIE Proceedings Vol. 11317:
Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Andrzej Krol; Barjor S. Gimi, Editor(s)

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