Improving Neuropathological Reconstruction Fidelity via AI Slice Imputation

TL;DR

This paper presents a super-resolution AI method that improves the fidelity of 3D neuropathological reconstructions from 2D photographs, enabling more accurate anatomical analysis and better integration with neuroimaging data.

Contribution

The authors introduce a novel, efficient slice imputation technique trained on synthetic data to enhance the resolution and accuracy of 3D reconstructions from dissection photographs.

Findings

Higher Dice scores in automated segmentation

More accurate cortical surface reconstructions

Improved MRI registration accuracy

Abstract

Neuropathological analyses benefit from spatially precise volumetric reconstructions that enhance anatomical delineation and improve morphometric accuracy. Our prior work has shown the feasibility of reconstructing 3D brain volumes from 2D dissection photographs. However these outputs sometimes exhibit coarse, overly smooth reconstructions of structures, especially under high anisotropy (i.e., reconstructions from thick slabs). Here, we introduce a computationally efficient super-resolution step that imputes slices to generate anatomically consistent isotropic volumes from anisotropic 3D reconstructions of dissection photographs. By training on domain-randomized synthetic data, we ensure that our method generalizes across dissection protocols and remains robust to large slab thicknesses. The imputed volumes yield improved automated segmentations, achieving higher Dice scores, particularly in cortical and white matter regions. Validation on surface reconstruction and atlas registration tasks demonstrates more accurate cortical surfaces and MRI registration. By enhancing the resolution and anatomical fidelity of photograph-based reconstructions, our approach strengthens the bridge between neuropathology and neuroimaging. Our method is publicly available at https://surfer.nmr.mgh.harvard.edu/fswiki/mri_3d_photo_recon