Many modern advanced biology experiments are carried on by Electron Microscope(EM) image analysis. Segmentation is one of the most important and complex steps in the process of image analysis. Previous ISBI contest results and related research show that Convolution Neural Network(CNN)has high classification accuracy in EM image segmentation. Besides it eliminates the pain of extracting complex features which’s indispensable for traditional classification algorithms. However CNN’s extremely time-consuming and fault vulnerability due to long time execution prevent it from being widely used in practice. In this paper, we try to address these problems by providing reliable high performance CNN framework for medial image segmentation.
Our CNN has light weighted user level checkpoint, which costs seconds when doing one checkpoint and restart. On the fact of lacking in platform diversity in current parallel CNN framework, our CNN system tries to make it general by providing distributed cross-platform parallelism implementation. Currently we have integrated Theano’s GPU implementation in our CNNsystem, and we explore parallelism potential on multi-core CPUs and many-core Intel Phi by testing performance of main kernel functions of CNN. In the future, we will integrate implementation son other two platforms into our CNN framework.