A Novel Multi-Scale Channel Attention-Guided Neural Network for Brain Stroke Lesion Segmentation

Abstract

Post-stroke neuroimaging is the key to the treatment of brain stroke. Typically, segmenting lesions manually is not only time-consuming, but limited by the varying morphology of lesions and the similarity of tissue intensity distribution. In recent years, with the rapidly and widely applying of deep learning technology in medical imaging, it becomes the one of the hot-spots. However, most stroke segmentation techniques could not utilize the structural symmetry information efficiently. Moreover, there are several problems, such as diverse lesions location, large changes of scale and unclear boundaries of lesions. To address these shortcomings, this paper presents a novel stroke segmentation model, called BSSNet. Firstly, the standard convolution of U-Net is replaced by the Depth-Wise Separable Convolution(DWSC) and the residual connection operation is used to reduce the loss of feature information in the encoder and decoder. Secondly, between the encoder and decoder, this paper introduces the Multi-Scale Channel Attention(MSCA) module to effectively improve the segmentation performance. Finally, the Attention-Guided Connection(AGC) module takes place of the original connection operation, which can select more context information from features with low-level guided by the features with high-level. To demonstrate the advantages of the proposed BSSNet, the comparison experiments are conducted on the open Anatomical Tracings of Lesions After Stroke(ATLAS) datasets. Experiment results shows that our model outperforms the state-of-the-art segmentation methods both in the quantitative metrics and visual effects.

Keywords

• Stroke lesion segmentation
• ATLAS datasets
• depth-wise separable convolution
• multi-scale channel attention
• attention-guided connection