A Computational Protocol for Whole Brain Histology Imaging

Recent advancements in tissue clearing and multiplex labeling have enabled the rapid acquisition of high-resolution, whole-brain 3D imagery [3]. However, the resulting datasets are often massive in size and structurally complex, creating significant bottlenecks in data storage, processing, and analysis. Here, we propose a robust, end-to-end pipeline designed to manage and analyze these large-scale volumes efficiently. This protocol utilizes a chunk-based data structure (OME-Zarr) to drastically lower hardware memory requirements [2], enabling the processing of terabyte-scale data on standard workstations. We design a workflow that integrates artifact correction, automated deep learning segmentation, and atlas registration. Furthermore, the pipeline facilitates dynamic, multiscale 3D visualization via a web-based interface (Neuroglancer) [7], allowing for the remote exploration of massive datasets without the need for complete data transfer.