MID-UNet: Multi-input directional UNet for COVID-19 lung infection segmentation from CT images

Jianning Chi; Shuang Zhang; Xiaoying Han; Huan Wang; Chengdong Wu; Xiaosheng Yu

doi:10.1016/j.image.2022.116835

MID-UNet: Multi-input directional UNet for COVID-19 lung infection segmentation from CT images

Signal Process Image Commun. 2022 Oct:108:116835. doi: 10.1016/j.image.2022.116835. Epub 2022 Aug 2.

Authors

Jianning Chi¹, Shuang Zhang¹, Xiaoying Han¹, Huan Wang¹, Chengdong Wu¹, Xiaosheng Yu¹

Affiliation

¹ Northeastern University, NO. 195, Chuangxin Road, Hunnan District, Shenyang, China.

Abstract

Coronavirus Disease 2019 (COVID-19) has spread globally since the first case was reported in December 2019, becoming a world-wide existential health crisis with over 90 million total confirmed cases. Segmentation of lung infection from computed tomography (CT) scans via deep learning method has a great potential in assisting the diagnosis and healthcare for COVID-19. However, current deep learning methods for segmenting infection regions from lung CT images suffer from three problems: (1) Low differentiation of semantic features between the COVID-19 infection regions, other pneumonia regions and normal lung tissues; (2) High variation of visual characteristics between different COVID-19 cases or stages; (3) High difficulty in constraining the irregular boundaries of the COVID-19 infection regions. To solve these problems, a multi-input directional UNet (MID-UNet) is proposed to segment COVID-19 infections in lung CT images. For the input part of the network, we firstly propose an image blurry descriptor to reflect the texture characteristic of the infections. Then the original CT image, the image enhanced by the adaptive histogram equalization, the image filtered by the non-local means filter and the blurry feature map are adopted together as the input of the proposed network. For the structure of the network, we propose the directional convolution block (DCB) which consist of 4 directional convolution kernels. DCBs are applied on the short-cut connections to refine the extracted features before they are transferred to the de-convolution parts. Furthermore, we propose a contour loss based on local curvature histogram then combine it with the binary cross entropy (BCE) loss and the intersection over union (IOU) loss for better segmentation boundary constraint. Experimental results on the COVID-19-CT-Seg dataset demonstrate that our proposed MID-UNet provides superior performance over the state-of-the-art methods on segmenting COVID-19 infections from CT images.

Keywords: COVID-19; CT image; Convolutional neural networks; Deep learning; Infection segmentation.