Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology

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openAccess
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2024-06-19
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Mcode
Degree programme
Language
en
Pages
23
Series
eLife, Volume 12, pp. 1-23
Abstract
We present open-source tools for three-dimensional (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: (1) 3D reconstruct a volume from the photographs and, optionally, a surface scan; and (2) produce a high-resolution 3D segmentation into 11 brain regions per hemisphere (22 in total), 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 measure-ments 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’ (https://surfer.nmr.mgh.harvard.edu/fswiki/ PhotoTools).
Description
Publisher Copyright: © 2023, Gazula et al.
Keywords
dissection photography, human, machine learning, neuroscience, surface scanning, volumetry
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Citation
Gazula, H, Tregidgo, H F J, Billot, B, Balbastre, Y, Williams-Ramirez, J, Herisse, R, Deden-Binder, L J, Casamitjana, A, Melief, E J, Latimer, C S, Kilgore, M D, Montine, M, Robinson, E, Blackburn, E, Marshall, M S, Connors, T R, Oakley, D H, Frosch, M P, Young, S I, Van Leemput, K, Dalca, A V, Fischl, B, MacDonald, C L, Keene, C D, Hyman, B T & Iglesias, J E 2024, ' Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology ', eLife, vol. 12, RP91398, pp. 1-23 . https://doi.org/10.7554/eLife.91398