3D Reconstruction and Segmentation of Dissection Photographs for MRI-free Neuropathology

TL;DR

This paper introduces a novel method to reconstruct and segment 3D brain volumes from dissection photographs, enabling MRI-free neuroimaging analysis and neuropathology correlation, especially useful when MRI data is unavailable or impractical.

Contribution

It presents a joint registration framework for 3D reconstruction and a Bayesian segmentation method for dissection photographs, expanding neuroimaging capabilities without MRI.

Findings

Achieved high Dice scores and volume correlations in 24 brains.

Demonstrated dissection photography as a valid MRI alternative for volumetric analysis.

Enabled retrospective analysis using existing photographic data.

Abstract

Neuroimaging to neuropathology correlation (NTNC) promises to enable the transfer of microscopic signatures of pathology to in vivo imaging with MRI, ultimately enhancing clinical care. NTNC traditionally requires a volumetric MRI scan, acquired either ex vivo or a short time prior to death. Unfortunately, ex vivo MRI is difficult and costly, and recent premortem scans of sufficient quality are seldom available. To bridge this gap, we present methodology to 3D reconstruct and segment full brain image volumes from brain dissection photographs, which are routinely acquired at many brain banks and neuropathology departments. The 3D reconstruction is achieved via a joint registration framework, which uses a reference volume other than MRI. This volume may represent either the sample at hand (e.g., a surface 3D scan) or the general population (a probabilistic atlas). In addition, we present a Bayesian method to segment the 3D reconstructed photographic volumes into 36 neuroanatomical structures, which is robust to nonuniform brightness within and across photographs. We evaluate our methods on a dataset with 24 brains, using Dice scores and volume correlations. The results show that dissection photography is a valid replacement for ex vivo MRI in many volumetric analyses, opening an avenue for MRI-free NTNC, including retrospective data. The code is available at https://github.com/htregidgo/DissectionPhotoVolumes.