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.

中文翻译：

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CDeep3M-基于即插即用的基于云的深度学习，用于图像分割

随着生物医学成像数据集的扩展，深层神经网络被认为对图像处理至关重要，然而，通过设置复杂的计算环境和高性能计算资源的可用性，社区访问仍然受到限制。我们使用CDeep3M解决了这些瓶颈，CDeep3M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，X射线和电子显微镜的大型和复杂的二维和三维成像数据集上对CDeep3M进行基准测试。