  The patient with ischemic stroke can benefit most from the earliest possible
definitive diagnosis. While the high quality medical resources are quite scarce
across the globe, an automated diagnostic tool is expected in analyzing the
magnetic resonance (MR) images to provide reference in clinical diagnosis. In
this paper, we propose a deep learning method to automatically segment ischemic
stroke lesions from multi-modal MR images. By using atrous convolution and
global convolution network, our proposed residual-structured fully
convolutional network (Res-FCN) is able to capture features from large
receptive fields. The network architecture is validated on a large dataset of
212 clinically acquired multi-modal MR images, which is shown to achieve a mean
dice coefficient of 0.645 with a mean number of false negative lesions of
1.515. The false negatives can reach a value that close to a common medical
image doctor, making it exceptive for a real clinical application.
