| Literature DB >> 30171236 |
Matthias G Haberl1,2, Christopher Churas3, Lucas Tindall4, Daniela Boassa4, Sébastien Phan4,3, Eric A Bushong4, Matthew Madany4, Raffi Akay4, Thomas J Deerinck4, Steven T Peltier4,3, Mark H Ellisman5,6.
Abstract
As biomedical imaging datasets expand, deep neural networks are considered vital for image processing, yet community access is still limited by setting up complex computational environments and availability of high-performance computing resources. We address these bottlenecks with CDeep3M, a ready-to-use image segmentation solution employing a cloud-based deep convolutional neural network. We benchmark CDeep3M on large and complex two-dimensional and three-dimensional imaging datasets from light, X-ray, and electron microscopy.Entities:
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Year: 2018 PMID: 30171236 PMCID: PMC6548193 DOI: 10.1038/s41592-018-0106-z
Source DB: PubMed Journal: Nat Methods ISSN: 1548-7091 Impact factor: 28.547