论文标题
Gorda：基于图的方向分布分析神经纤维的SLI散射法模式
GORDA: Graph-based ORientation Distribution Analysis of SLI scatterometry Patterns of Nerve Fibres
论文作者
论文摘要
散射的光成像（SLI）是一种新颖的方法，用于显微揭示未染色的脑切片的纤维结构。测量是通过从不同角度照亮脑切片并在正常发射率下测量发射（分散的）光获得的。散射曲线的评估通常依赖于峰采摘技术和特征从峰中提取的特征，从而可以定量确定每个图像像素的平行和跨平面内神经纤维方向。但是，无法通过传统方法来评估纤维3D方向的估计。我们提出了一种无监督的学习方法，使用球形卷积来估计神经纤维的3D取向，从而对大脑中的纤维取向分布进行了更详细的解释。
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.
