Efficient U-Net CNN with Data Augmentation for MRI Ischemic Stroke Brain Segmentation

Ischemic stroke brains are common cerebrovascular diseases and one of the major issues of long-term disabilities and mortalities in the world. The Detection of ischemic stroke lesions has a significant role in the diagnosis process. In terms of timing and accuracy, automated biomedical segmentation from a magnetic resonance imaging modalities has shown to be quite beneficial. In this work, we suggest a deep convolutional neural network (CNN) approach based on U-Net architecture that is able to separate ischemic stroke lesions from normal tissue. Fine-tuning technique has been applied to adopt our aims with U-Net architecture. We used a public dataset ISLES 2015 to evaluate the proposed approach. Experimentally, our network achieved an average Dice Coefficient (DC), and accuracy is 55.77%, 99.96% respectively. Quantitative measures show that U-Net CNN provides significant evaluation metrics. Our proposed network could be used to create an automated tool to segment ischemic stroke brain lesions.