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1.
Nat Methods ; 15(9): 677-680, 2018 09.
Artículo en Inglés | MEDLINE | ID: mdl-30171236

RESUMEN

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.


Asunto(s)
Nube Computacional , Aprendizaje Profundo , Procesamiento de Imagen Asistido por Computador/métodos
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