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Perivascular Space Semi-Automatic Segmentation (PVSSAS): A Tool for Segmenting, Viewing and Editing Perivascular Spaces

Derek A Smith, Gaurav Verma, Daniel Ranti, Matthew Markowitz, Priti Balchandani, Laurel Morris
doi: https://doi.org/10.1101/2020.11.16.385336
Derek A Smith
aIcahn School of Medicine at Mount Sinai, Hess CSM Building (1470 Madison Avenue, New York, NY, 10029)
bBioMedical Engineering and Imaging Institute at Mount Sinai School of Medicine, Leon and Norma Hess Center for Science and Medicine, 1470 Madison Avenue, New York, NY 10029
dDepartment of Radiology at the Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1234, New York, NY 10029
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  • For correspondence: derekalexander.smith@mssm.edu
Gaurav Verma
aIcahn School of Medicine at Mount Sinai, Hess CSM Building (1470 Madison Avenue, New York, NY, 10029)
bBioMedical Engineering and Imaging Institute at Mount Sinai School of Medicine, Leon and Norma Hess Center for Science and Medicine, 1470 Madison Avenue, New York, NY 10029
dDepartment of Radiology at the Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1234, New York, NY 10029
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Daniel Ranti
aIcahn School of Medicine at Mount Sinai, Hess CSM Building (1470 Madison Avenue, New York, NY, 10029)
bBioMedical Engineering and Imaging Institute at Mount Sinai School of Medicine, Leon and Norma Hess Center for Science and Medicine, 1470 Madison Avenue, New York, NY 10029
fDepartment of Psychiatry at the Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1230, New York, NY 10029
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Matthew Markowitz
gAdvanced Science Research Center at The Graduate Center of the City University of New York, 85 St. Nicholas Terrace, New York, NY 10031
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Priti Balchandani
aIcahn School of Medicine at Mount Sinai, Hess CSM Building (1470 Madison Avenue, New York, NY, 10029)
bBioMedical Engineering and Imaging Institute at Mount Sinai School of Medicine, Leon and Norma Hess Center for Science and Medicine, 1470 Madison Avenue, New York, NY 10029
cFishberg Department of Neuroscience at the Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029
dDepartment of Radiology at the Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1234, New York, NY 10029
eEstelle and Daniel Maggin Department of Neurology at the Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1137, New York, NY 10029
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Laurel Morris
aIcahn School of Medicine at Mount Sinai, Hess CSM Building (1470 Madison Avenue, New York, NY, 10029)
bBioMedical Engineering and Imaging Institute at Mount Sinai School of Medicine, Leon and Norma Hess Center for Science and Medicine, 1470 Madison Avenue, New York, NY 10029
dDepartment of Radiology at the Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1234, New York, NY 10029
fDepartment of Psychiatry at the Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1230, New York, NY 10029
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Summary

Objective In this study, we validate and describe a user-friendly tool for PVS tracing that uses a Frangi-based detection algorithm; which will be made freely available to aid in future clinical and research applications. All PVS detected by the semi-automated method had a match with the manual dataset and 94% of the manual PVS had a match within the semi-automated dataset.

Methods We deployed a Frangi-based filter using a pre-existing Matlab toolbox. The PVSSAS tool pre-processes the images and is optimized for maximum effectiveness in this application. A user-friendly GUI was developed to aid the speed and ease in marking large numbers of PVS across the entire brain at once.

Results Using a tolerance of 0.7 cm, 83% of all PVSs detected by the semi-automated method had a match with the manual dataset and 94% of the manual PVS had a match within the semi-automated dataset. As shown in figure 3, there was generally excellent agreement between the manual and semi-automated markings in any given slice.

Significance The primary benefit of PVSSAS will be time saved marking PVS. Clinical MRI use is likely to become more widespread in the diagnosis, treatment, and study of MS and other degenerative neurological conditions in the coming years. Tools like the one presented here will be invaluable in ensuring that the tracing and quantitative analysis of these PVS does not act as a bottle neck to treatment and further research.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted November 17, 2020.
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Perivascular Space Semi-Automatic Segmentation (PVSSAS): A Tool for Segmenting, Viewing and Editing Perivascular Spaces
Derek A Smith, Gaurav Verma, Daniel Ranti, Matthew Markowitz, Priti Balchandani, Laurel Morris
bioRxiv 2020.11.16.385336; doi: https://doi.org/10.1101/2020.11.16.385336
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Perivascular Space Semi-Automatic Segmentation (PVSSAS): A Tool for Segmenting, Viewing and Editing Perivascular Spaces
Derek A Smith, Gaurav Verma, Daniel Ranti, Matthew Markowitz, Priti Balchandani, Laurel Morris
bioRxiv 2020.11.16.385336; doi: https://doi.org/10.1101/2020.11.16.385336

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