CDSE-UNet: Enhancing COVID-19 CT Image Segmentation with Canny Edge
  Detection and Dual-Path SENet Feature Fusion
Jiao Ding Jie Chang Renrui Han Li Yang
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