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Chunkflow: hybrid cloud processing of large 3D images by convolutional nets

Author(s): Wu, Jingpeng; Silversmith, William M; Lee, Kisuk; Seung, H Sebastian

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dc.contributor.authorWu, Jingpeng-
dc.contributor.authorSilversmith, William M-
dc.contributor.authorLee, Kisuk-
dc.contributor.authorSeung, H Sebastian-
dc.date.accessioned2021-10-08T19:51:12Z-
dc.date.available2021-10-08T19:51:12Z-
dc.date.issued2021en_US
dc.identifier.citationWu, Jingpeng, William M. Silversmith, Kisuk Lee, and H. Sebastian Seung. "Chunkflow: hybrid cloud processing of large 3D images by convolutional nets." Nature Methods 18, no. 4 (2021): pp. 328-330. doi:10.1038/s41592-021-01088-5en_US
dc.identifier.issn1548-7105-
dc.identifier.urihttps://arxiv.org/pdf/1904.10489.pdf-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr16k07-
dc.description.abstractAutomated microscopes with both high resolution and large field of view are generating terascale and even petascale 3D images. A local cluster might not have enough computational resources to process them in reasonable time, but public cloud platforms can provide computational resources on demand. Convolutional networks have become the state-of-the-art approach for 3D biological image analysis1,2 , and cloud processing by 3D convolutional nets has been used for processing independent small image stacks3–5 . However, cloud computing tools to perform distributed processing of terascale or petascale 3D images by convolutional nets are lacking. Here, we report chunkflow, a framework for distributing computational tasks over both cloud and local computational resources, including both GPUs and CPUs with multiple deep-learning framework back ends, to maximize efficiency, increase flexibility and reduce cost.en_US
dc.format.extent328 - 330en_US
dc.language.isoen_USen_US
dc.relation.ispartofNature Methodsen_US
dc.rightsAuthor's manuscripten_US
dc.titleChunkflow: hybrid cloud processing of large 3D images by convolutional netsen_US
dc.typeJournal Articleen_US
dc.identifier.doi10.1038/s41592-021-01088-5-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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