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3D-MAESTRO: A scalable, modular, portable pipeline for automated processing of large-scale volumetric brain microscopy data

Laiton, C.; Lusk, N.; Browning, Y.; Summers, M.; Taormina, M.; Wang, D.; Siegle, J.; Myers, H.; Tan, B.; Kosillo, P.; Peterson, E.; Toglia, D.; Lakunina, A.; Burckhardt, S.; Young, J.; Kapoor, M.; Wang, T.; Rohde, J.; Lynch, G.; Yao, S.; Narayan, S.; Hooper, M.; Way, S.; Waters, J.; Tasic, B.; Chandrashekar, J.; Glaser, A.; Feng, D.; Seshamani, S.; Svoboda, K.

2026-09-15 neuroscience
10.64898/2026.09.09.750494 bioRxiv
Show abstract

Light microscopy is routinely used to explore the cellular and molecular structure of tissues, but the scale and complexity of data remains a bottleneck for discovery. We introduce 3D-MAESTRO (3D-Microscopy Automation and Execution with Scalable Tools, Rendering, and Orchestration): an automated image processing workflow for large-scale microscopy data, built for scalable execution across cloud and local computing environments. 3D-MAESTRO orchestrates denoising, stitching of image tiles, atlas registration, and segmentation. Its modular architecture permits the integration and benchmarking of new packages, ensuring that performance evolves as more efficient or accurate algorithms emerge. We introduce a new 3D image template for automated registration of mouse brains cleared with aqueous reagents and an efficient method for detection of fluorescent cells. We apply 3D-MAESTRO to lightsheet images of whole mouse brains in the context of diverse anatomical and functional experiments, illustrating high-throughput and reproducible mapping of microscopic structures across the brain.

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