Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology
| Title: | Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology |
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| Authors: | Harshvardhan Gazula, Henry FJ Tregidgo, Benjamin Billot, Yael Balbastre, Jonathan Williams-Ramirez, Rogeny Herisse, Lucas J Deden-Binder, Adria 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 MacDonald, C Dirk Keene, Bradley T Hyman, Juan E Iglesias |
| Source: | eLife, Vol 12 (2024) |
| Publisher Information: | eLife Sciences Publications Ltd, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Medicine LCC:Science LCC:Biology (General) |
| Subject Terms: | dissection photography, machine learning, surface scanning, volumetry, Medicine, Science, Biology (General), QH301-705.5 |
| More Details: | 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 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). |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2050-084X |
| Relation: | https://elifesciences.org/articles/91398; https://doaj.org/toc/2050-084X |
| DOI: | 10.7554/eLife.91398 |
| Access URL: | https://doaj.org/article/bee5031ed3ac4569bd31db0c5630fc96 |
| Accession Number: | edsdoj.bee5031ed3ac4569bd31db0c5630fc96 |
| Database: | Directory of Open Access Journals |
| ISSN: | 2050084X |
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| DOI: | 10.7554/eLife.91398 |
| Published in: | eLife |
| Language: | English |