Attention convolutional neural network for accurate segmentation and quantification of lesions in ischemic stroke disease

Abstract

Ischemic stroke lesion and white matter hyperintensity (WMH) lesion appear as regions of abnormally signal intensity on magnetic resonance image (MRI) sequences. Ischemic stroke is a frequent cause of death and disability, while WMH is a risk factor for stroke. Accurate segmentation and quantification of ischemic stroke and WMH lesions are important for diagnosis and prognosis. However, radiologists have a difficult time distinguishing these two types of similar lesions. A novel deep residual attention convolutional neural network (DRANet) is proposed to accurately and simultaneously segment and quantify ischemic stroke and WMH lesions in the MRI images. DRANet inherits the advantages of the U-net design and applies a novel attention module that extracts high-quality features from the input images. Moreover, the Dice loss function is used to train DRANet to address data imbalance in the training data set. DRANet is trained and evaluated on 742 2D MRI images which are produced from the sub-acute ischemic stroke lesion segmentation (SISS) challenge. Empirical tests demonstrate that DRANet outperforms several other state-of-the-art segmentation methods. It accurately segments and quantifies both ischemic stroke lesion and WMH. Ablation experiments reveal that attention modules improve the predictive performance of DRANet.