| Autor: |
Haberl, Matthias G, Churas, Christopher, Tindall, Lucas, Boassa, Daniela, Phan, Sébastien, Bushong, Eric A, Madany, Matthew, Akay, Raffi, Deerinck, Thomas J, Peltier, Steven T, Ellisman, Mark H |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
Nature methods, vol 15, iss 9 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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