Normal brain volume measurements using multispectral MRI segmentation
Abstract
The performance of a supervised k-nearest neighbor (kNN) classifier and a semisupervised fuzzy c-means (SFCM) clustering segmentation method are evaluated for reproducible measurement of the volumes of normal brain tissues and cerebrospinal fluid. The stability of the two segmentation methods is evaluated for (a) operator selection of training data, (b) reproducibility during repeat imaging sessions to determine any variations in the sensor performance over time, (c) variations in the measured volumes between different subjects, and (d) variability with different imaging parameters. The variations were found to be dependent on the type of measured tissue and the operator performing the segmentations. The variability during repeat imaging sessions for the SFCM method was < 3%. The absolute volumes of the brain matter and cerebrospinal fluid between subjects varied quite large, ranging from 9% to 13%. The intraobserver and interobserver reproducibility for SFCM were < 4% for the soft tissues and 6% for cerebrospinal fluid. The corresponding results for the kNN segmentation method were higher compared to the SFCM method.
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Experimental data has provided a wide range of measured human intracranial volumes (1152-1839 cm3) and total average cerebral spinal fluid (CSF) volume (82-125.3 cm3), with a total brain-cell parenchyma volume of 1319 cm3, including 489 cm3 of white matter and 786 cm3 of gray matter.
“Experimental data has provided a wide range of measured human intracranial volumes (1152–1839 cm3) and total average cerebral spinal fluid (CSF) volume (82–125.3 cm3), with a total brain-cell parenchyma volume of 1319 cm3, including 489 cm3 of white matter and 786 cm3 of gray matter (Vaidyanathana et al., 1997; Nopoulos et al., 2000).”
From Human Brain/Cloud Interface (2019) · cites this paper as Vaidyanathana et al., 1997
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Martins, N. R. B., Angelica, A., Chakravarthy, K., Svidinenko, Y., Boehm, F. J., Opris, I., Lebedev, M. A., Swan, M., Garan, S. A., Rosenfeld, J. V., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00112 · Cited by 91 · see citing works
- Authors
- Vaidyanathan, M., Clarke, L. P., Heidtman, C., Velthuizen, R. P., & Hall, L. O.
- Affiliations
- University of South Florida (US)
- Corresponding author
- Clarke, L.P.
- Venue
- Magnetic Resonance Imaging
- Publisher
- Elsevier BV
- Published
- 1997-01
- Volume
- 15
- Issue
- 1
- Pages
- 87-97
- ISSN
- 0730-725X (ISSN-L)
- OpenAlex
- W2037352762
- Cited by
- 39
- References
- 0
- Citations by year
- 2024: 1 · 2023: 1 · 2021: 1 · 2019: 2 · 2016: 1 · 2015: 2 · 2014: 1 · 2012: 1
- FWCI
- 5.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Magnetic Resonance Imaging (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5r01ca059425-03)
- National Cancer Institute (1R01CA5942501)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- brain tissue segmentation, cerebrospinal fluid, brain volumetry, fuzzy c-means clustering, k-nearest neighbors, multispectral MRI, reproducibility, interobserver variability, intraobserver variability, normal brain tissue
- MeSH terms
- Adult, Algorithms, Brain, Cerebrospinal Fluid, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Pattern Recognition, Automated, Reproducibility of Results, Observer Variation, Selection Bias, Fuzzy Logic, Phantoms, Imaging
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