Efficient image analysis for large-scale next generation histopathology using pAPRica

Scholler, J.; Jonsson, J.; Jorda-Siquier, T.; Gantar, I.; Batti, L.; Cheeseman, B.; Pages, S.; Sbalzarini, I. F.; Lamy, C. M.

doi:10.1101/2023.01.27.525687

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bioRxiv · 10.1101/2023.01.27.525687

Efficient image analysis for large-scale next generation histopathology using pAPRica

Scholler, J.·Jonsson, J.·Jorda-Siquier, T.·Gantar, I.·Batti, L.·Cheeseman, B.·Pages, S.·Sbalzarini, I. F.·Lamy, C. M.

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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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Scholler, J., Jonsson, J., Jorda-Siquier, T., Gantar, I., Batti, L., Cheeseman, B., Pages, S., Sbalzarini, I. F., Lamy, C. M.. 2023-01-28. Efficient image analysis for large-scale next generation histopathology using pAPRica. https://doi.org/10.1101/2023.01.27.525687

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