%0 Unpublished Work %1 Scholler2023-om %A Scholler, Jules %A Jonsson, Joel %A Jordá-Siquier, Tomás %A Gantar, Ivana %A Batti, Laura %A Cheeseman, Bevan L %A Pagès, Stéphane %A Sbalzarini, Ivo F %A Lamy, Christophe M %D 2023 %J bioRxiv %K Efficient Yaff analysis generation histopathology image large-scale {pAPRica} %T Efficient image analysis for large-scale next generation histopathology using pAPRica %X AbstractThe 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.

@unpublished{Scholler2023-om, abstract = {AbstractThe large size of imaging datasets generated by next-generation histology methods limits the adoption of those approaches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.}, added-at = {2025-01-07T14:31:47.000+0100}, author = {Scholler, Jules and Jonsson, Joel and Jord{\'a}-Siquier, Tom{\'a}s and Gantar, Ivana and Batti, Laura and Cheeseman, Bevan L and Pag{\`e}s, St{\'e}phane and Sbalzarini, Ivo F and Lamy, Christophe M}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2a7df3e6cf0f53ed27e93a75df3fd3012/scadsfct}, interhash = {d1f81c66db43d9ba8acb7ff2f03c949c}, intrahash = {a7df3e6cf0f53ed27e93a75df3fd3012}, journal = {bioRxiv}, keywords = {Efficient Yaff analysis generation histopathology image large-scale {pAPRica}}, month = jan, timestamp = {2025-01-31T12:01:26.000+0100}, title = {Efficient image analysis for large-scale next generation histopathology using {pAPRica}}, year = 2023 }