GORDA: Graph-based ORientation Distribution Analysis of SLI scatterometry Patterns of Nerve Fibres

TL;DR

This paper introduces GORDA, an unsupervised spherical convolution method for analyzing 3D nerve fibre orientations in brain tissue using Scatter Light Imaging, enhancing the understanding of neural architecture beyond traditional peak-based techniques.

Contribution

The paper presents a novel unsupervised learning approach with spherical convolutions for 3D fibre orientation estimation in SLI data, improving detail and accuracy.

Findings

Effective 3D orientation estimation from SLI data.

Enhanced interpretation of nerve fibre architecture.

Outperforms traditional peak-based analysis methods.

Abstract

Scattered Light Imaging (SLI) is a novel approach for microscopically revealing the fibre architecture of unstained brain sections. The measurements are obtained by illuminating brain sections from different angles and measuring the transmitted (scattered) light under normal incidence. The evaluation of scattering profiles commonly relies on a peak picking technique and feature extraction from the peaks, which allows quantitative determination of parallel and crossing in-plane nerve fibre directions for each image pixel. However, the estimation of the 3D orientation of the fibres cannot be assessed with the traditional methodology. We propose an unsupervised learning approach using spherical convolutions for estimating the 3D orientation of neural fibres, resulting in a more detailed interpretation of the fibre orientation distributions in the brain.