Convolutional neural networks are the way to solve arbitrary image segmentation tasks. However, when images are large, memory demands often exceed the available resources, in particular on a common GPU. Especially in biomedical imaging, where 3D images are common, the problems are apparent. A typical approach to solve this limitation is to break the task into smaller subtasks by dividing images into smaller image patches. Another approach, if applicable, is to look at the 2D image sections separately, and to solve the problem in 2D. Often, the loss of global context makes such approaches less effective; important global information might not be present in the current image patch, or the selected 2D image section. Here, we propose Deep Neural Patchworks (DNP), a segmentation framework that is based on hierarchical and nested stacking of patch-based networks that solves the dilemma between global context and memory limitations.

翻译：革命神经网络是解决任意图像分割任务的方法。 但是,如果图像巨大,记忆需求往往超过现有资源,特别是通用 GPU。 特别是在生物医学成像中,3D图像是常见的,问题显而易见。 解决这一限制的典型办法是将图像分成较小的图像补丁,将任务分成较小的子任务。 另一种办法,如果适用的话,是分别查看 2D 图像部分,并在 2D 中解决问题。 通常,全球背景的丧失会降低这种方法的效力;重要的全球信息可能不会出现在当前图像补丁或选定的 2D 图像部分。 在这里,我们提出深神经补丁(DNP), 这是一种基于分层网络的分层和嵌套堆, 解决全球背景和记忆限制之间的两难点。