Teravoxel Microscopy Image Analysis for Neurological Diseases.

Abstract

Light-sheet fluorescence microscopy (LSFM) has emerged as a revolutionary imaging modality for investigating intact three-dimensional brain structures at the teravoxel scale. In parallel, high-throughput computational methods, especially deep learning approaches, have opened new avenues for uncovering the pathophysiological mechanisms of neurological diseases through LSFM technology. Recent advances in optics and tissue clearing methods have allowed whole-brain imaging at cellular resolution in three dimensions, and the integration of artificial intelligence has facilitated the identification of disease-related cellular profiles and morphological markers. Machine learning techniques for stitching, segmentation, classification, super-resolution, and registration, therefore, are promoted to uncover biological patterns that are not visible to human eyes yet are related to neuroinflammatory and neurodegenerative diseases. However, analytic pipelines have been designed differently for various animal models and brain structures, leading to challenges in feasibility and compatibility within this emerging field of data-driven LSFM image analysis. Here, we present an overview of current pipelines, examine existing and forthcoming challenges as the LSFM community advances, demonstrate their implications for neurological disease applications, and propose potential solutions.