Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.01.27.525687v1?rss=1
Authors: Scholler, J., Jonsson, J., Jorda-Siquier, T., Gantar, I., Batti, L., Cheeseman, B., Pages, S., Sbalzarini, I. F., Lamy, C. M.
Abstract:
The large size of imaging datasets generated by next-generation histology methods limits the adoption of those approaches in research and the clinic. We propose pAPRica (pipelines for Adaptive Particle Representation image compositing and analysis), a framework based on the Adaptive Particle Representation (APR) to enable efficient analysis of large microscopy datasets, scalable up to petascale on a regular workstation. pAPRica includes stitching, merging, segmentation, registration, and mapping to an atlas as well as visualization of the large 3D image data, achieving 100+ fold speedup in computation and commensurate data-size reduction.
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