CDeep3M——基于云的图像分割深度神经网络插件。
CDeep3M-Plug-and-Play cloud-based deep learning for image segmentation.
机构信息
National Center for Microscopy and Imaging Research, School of Medicine, University of California San Diego, La Jolla, CA, USA.
National Biomedical Computation Resource, University of California San Diego, La Jolla, CA, USA.
出版信息
Nat Methods. 2018 Sep;15(9):677-680. doi: 10.1038/s41592-018-0106-z. Epub 2018 Aug 31.
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.
摘要
随着生物医学成像数据集的不断扩大，深度神经网络被认为对图像处理至关重要，但由于需要搭建复杂的计算环境和获取高性能计算资源，社区的访问仍然受到限制。我们使用 CDeep3M 解决了这些瓶颈问题，这是一种使用基于云的深度卷积神经网络的即用型图像分割解决方案。我们在来自光学、X 射线和电子显微镜的大型和复杂二维和三维成像数据集上对 CDeep3M 进行了基准测试。
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