A Novel Network for Context Feature Extraction in the Field of Medical Image Segmentation

In this study, a novel model called CFE-UNet has been proposed for extracting the context feature to improve the model performance with moderate parameters and computational cost in the field of medical image segmentation. The proposed model comprises two core components, which are context-aware module and hierarchical neighborhood feature extraction module, respectively. These components are instrumental in context feature extraction, thereby enhancing the model's performance. In addition, a dataset comprising DWI images of acute ischemic stroke (AIS) has been proposed for testing in this study. Extensive experiments have been conducted on the proposed dataset together with four other commonly used datasets, which are ISIC 2018, BUSI, GlaS, and Kvasir-SEG, respectively. The promising results demonstrate that the proposed CFE-UNet model achieves exceptional performance with moderate model parameters and computational costs.