CDSE-UNet: Enhancing COVID-19 CT Image Segmentation with Canny Edge Detection and Dual-Path SENet Feature Fusion

TL;DR

This paper introduces CDSE-UNet, a novel segmentation model for COVID-19 CT images that combines edge detection, dual-path feature fusion, and multiscale convolution to improve accuracy in lesion segmentation.

Contribution

It presents a new UNet-based architecture integrating Canny edge detection, dual-path SENet feature fusion, and multiscale convolution for enhanced COVID-19 lesion segmentation.

Findings

Outperforms existing models in segmenting lesions of various sizes

Accurately delineates lesion edges and suppresses noise

Effective in both large and small lesion areas

Abstract

Accurate segmentation of COVID-19 CT images is crucial for reducing the severity and mortality rates associated with COVID-19 infections. In response to blurred boundaries and high variability characteristic of lesion areas in COVID-19 CT images, we introduce CDSE-UNet: a novel UNet-based segmentation model that integrates Canny operator edge detection and a dual-path SENet feature fusion mechanism. This model enhances the standard UNet architecture by employing the Canny operator for edge detection in sample images, paralleling this with a similar network structure for semantic feature extraction. A key innovation is the Double SENet Feature Fusion Block, applied across corresponding network layers to effectively combine features from both image paths. Moreover, we have developed a Multiscale Convolution approach, replacing the standard Convolution in UNet, to adapt to the varied lesion sizes and shapes. This addition not only aids in accurately classifying lesion edge pixels but also significantly improves channel differentiation and expands the capacity of the model. Our evaluations on public datasets demonstrate CDSE-UNet's superior performance over other leading models, particularly in segmenting large and small lesion areas, accurately delineating lesion edges, and effectively suppressing noise