# Enhanced Multiscale Human Brain Imaging by Semi-supervised Digital Staining and Serial Sectioning Optical Coherence Tomography

## TL;DR

This paper introduces a new 3D imaging method combining optical coherence tomography and digital staining to enhance brain imaging at multiple scales.

## Contribution

The novel semi-supervised learning technique enables digital staining of brain tissue using weakly paired images.

## Key findings

- Digital staining consistently improves contrast across cortical layer boundaries.
- Geometry-preserving 3D imaging is demonstrated on cubic-centimeter tissue blocks.
- Meso-scale vessel networks in white matter are visualized effectively.

## Abstract

A major challenge in neuroscience is to visualize the structure of the human brain at different scales. Traditional histology reveals micro- and meso-scale brain features, but suffers from staining variability, tissue damage and distortion that impedes accurate 3D reconstructions. Here, we present a new 3D imaging framework that combines serial sectioning optical coherence tomography (S-OCT) with a deep-learning digital staining (DS) model. We develop a novel semi-supervised learning technique to facilitate DS model training on weakly paired images. The DS model performs translation from S-OCT to Gallyas silver staining. We demonstrate DS on various human cerebral cortex samples with consistent staining quality. Additionally, we show that DS enhances contrast across cortical layer boundaries. Furthermore, we showcase geometry-preserving 3D DS on cubic-centimeter tissue blocks and visualization of meso-scale vessel networks in the white matter. We believe that our technique offers the potential for high-throughput, multiscale imaging of brain tissues and may facilitate studies of brain structures.