Fully automatic segmentation for ischemic stroke using CT perfusion maps

Abstract

We propose an algorithm for automatic segmentation of ischemic lesion using CT perfusion maps. Our method is based on encoder-decoder fully convolutional neural network approach. The pre-processing step involves skull stripping and standardization of perfusion maps and extraction of slices with lesions as the training data. These CT perfusion maps are used to train the proposed network for automatic segmentation of stroke lesions. The network is trained by minimizing the weighted combination of cross entropy and dice losses. Our algorithm achieves 0.43, 0.53 and 0.45 Dice, precision, and recall respectively on challenge test data set.