Author Response: Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology Article Swipe
Harshvardhan Gazula
,
Henry F. J. Tregidgo
,
Benjamin Billot
,
Yaël Balbastre
,
Jonathan William-Ramirez
,
Rogeny Herisse
,
Lucas J Deden-Binder
,
Adrià Casamitjana
,
Erica J. Melief
,
Caitlin S. Latimer
,
Mitchell D. Kilgore
,
Mark Montine
,
Eleanor Robinson
,
Emily Blackburn
,
Michael S. Marshall
,
Theresa R. Connors
,
Derek H. Oakley
,
Matthew P. Frosch
,
Sean I. Young
,
Koen Van Leemput
,
Adrian V. Dalca
,
Bruce Fischl
,
Christine L. Mac Donald
,
C. Dirk Keene
,
Bradley T. Hyman
,
Juan Eugenio Iglesias
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.7554/elife.91398.1.sa3
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.7554/elife.91398.1.sa3
We present open-source tools for 3D analysis of photographs of dissected slices of human brains, which are routinely acquired in brain banks but seldom used for quantitative analysis. Our tools can: (i) 3D reconstruct a volume from the photographs and, optionally, a surface scan; and (ii) produce a high-resolution 3D segmentation into 11 brain regions, independently of the slice thickness. Our tools can be used as a substitute for ex vivo magnetic resonance imaging (MRI), which requires access to an MRI scanner, ex vivo scanning expertise, and considerable financial resources. We tested our tools on synthetic and real data from two NIH Alzheimer’s Disease Research Centers. The results show that our methodology yields accurate 3D reconstructions, segmentations, and volumetric measurements that are highly correlated to those from MRI. Our method also detects expected differences between post mortem confirmed Alzheimer’s disease cases and controls. The tools are available in our widespread neuroimaging suite “FreeSurfer” ().
Related Topics
Concepts
Neuropathology
Segmentation
Artificial intelligence
Neuroimaging
Computer science
Magnetic resonance imaging
Scanner
Ex vivo
Computer vision
Suite
Neuroscience
Pattern recognition (psychology)
Biomedical engineering
Medicine
Radiology
Pathology
Psychology
In vivo
Biology
Disease
Geography
Biotechnology
Archaeology
Metadata
- Type
- peer-review
- Language
- en
- Landing Page
- https://doi.org/10.7554/elife.91398.1.sa3
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389615982
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389615982Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.7554/elife.91398.1.sa3Digital Object Identifier
- Title
-
Author Response: Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathologyWork title
- Type
-
peer-reviewOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-12Full publication date if available
- Authors
-
Harshvardhan Gazula, Henry F. J. Tregidgo, Benjamin Billot, Yaël Balbastre, Jonathan William-Ramirez, Rogeny Herisse, Lucas J Deden-Binder, Adrià Casamitjana, Erica J. Melief, Caitlin S. Latimer, Mitchell D. Kilgore, Mark Montine, Eleanor Robinson, Emily Blackburn, Michael S. Marshall, Theresa R. Connors, Derek H. Oakley, Matthew P. Frosch, Sean I. Young, Koen Van Leemput, Adrian V. Dalca, Bruce Fischl, Christine L. Mac Donald, C. Dirk Keene, Bradley T. Hyman, Juan Eugenio IglesiasList of authors in order
- Landing page
-
https://doi.org/10.7554/elife.91398.1.sa3Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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-
https://doi.org/10.7554/elife.91398.1.sa3Direct OA link when available
- Concepts
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Neuropathology, Segmentation, Artificial intelligence, Neuroimaging, Computer science, Magnetic resonance imaging, Scanner, Ex vivo, Computer vision, Suite, Neuroscience, Pattern recognition (psychology), Biomedical engineering, Medicine, Radiology, Pathology, Psychology, In vivo, Biology, Disease, Geography, Biotechnology, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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