DECE-Net: a dual-path encoder network with contour enhancement for pneumonia lesion segmentation.

Purpose: Early-stage pneumonia is not easily detected, leading to many patients missing the optimal treatment window. This is because segmenting lesion areas from CT images presents several challenges, including low-intensity contrast between the lesion and normal areas, as well as variations in the shape and size of lesion areas. To overcome these challenges, we propose a segmentation network called DECE-Net to segment the pneumonia lesions from CT images automatically. Approach: The DECE-Net adds an extra encoder path to the U-Net, where one encoder path extracts the features of the original CT image with the attention multi-scale feature fusion module, and the other encoder path extracts the contour features in the CT contour image with the contour feature extraction module to compensate and enhance the boundary information that is lost in the downsampling process. The network further fuses the low-level features from both encoder paths through the feature fusion attention connection module and connects them to the upsampled high-level features to replace the skip connections in the U-Net. Finally, multi-point deep supervision is applied to the segmentation results at each scale to improve segmentation accuracy. Results: We evaluate the DECE-Net using four public COVID-19 segmentation datasets. The mIoU results for the four datasets are 80.76%, 84.59%, 84.41%, and 78.55%, respectively. Conclusions: The experimental results indicate that the proposed DECE-Net achieves state-of-the-art performance, especially in the precise segmentation of small lesion areas.