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[Preprint]. 2023 Jul 24:2023.06.08.544050. Originally published 2023 Jun 9. [Version 2] doi: 10.1101/2023.06.08.544050

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

Harshvardhan Gazula, Henry F J Tregidgo, Benjamin Billot, Yael 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
PMCID: PMC10274889  PMID: 37333251

Abstract

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” ( https://surfer.nmr.mgh.harvard.edu/fswiki/PhotoTools ).

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