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

Abstract

AbstractWe 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 per hemisphere (22 in total), independently of the slice thickness. Our tools can be used as a substitute forex vivomagnetic resonance imaging (MRI), which requires access to an MRI scanner,ex vivoscanning 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 betweenpost mortemconfirmed Alzheimer’s disease cases and controls. The tools are available in our widespread neuroimaging suite “FreeSurfer” (https://surfer.nmr.mgh.harvard.edu/fswiki/PhotoTools).