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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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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