MS-DCANet: A Novel Segmentation Network For Multi-Modality COVID-19 Medical Images

Abstract

Xiaoyu Pan,1,* Huazheng Zhu,2,* Jinglong Du,1 Guangtao Hu,1 Baoru Han,1 Yuanyuan Jia1 1College of Medical Informatics, Chongqing Medical University, Chongqing, People’s Republic of China; 2College of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yuanyuan Jia, Email [email protected]: The Coronavirus Disease 2019 (COVID-19) pandemic has increased the public health burden and brought profound disaster to humans. For the particularity of the COVID-19 medical images with blurred boundaries, low contrast and different infection sites, some researchers have improved the accuracy by adding more complexity. Also, they overlook the complexity of lesions, which hinder their ability to capture the relationship between segmentation sites and the background, as well as the edge contours and global context. However, increasing the computational complexity, parameters and inference speed is unfavorable for model transfer from laboratory to clinic. A perfect segmentation network needs to balance the above three factors completely. To solve the above issues, this paper propose a symmetric automatic segmentation framework named MS-DCANet. We introduce Tokenized MLP block, a novel attention scheme that uses a shift-window mechanism to conditionally fuse local and global features to get more continuous boundaries and spatial positioning capabilities. It has greater understanding of irregular lesion contours. MS-DCANet also uses several Dual Channel blocks and a Res-ASPP block to improve the ability to recognize small targets. On multi-modality COVID-19 tasks, MS-DCANet achieved state-of-the-art performance compared with other baselines. It can well trade off the accuracy and complexity. To prove the strong generalization ability of our proposed model, we apply it to other tasks (ISIC 2018 and BAA) and achieve satisfactory results.Patients: The X-ray dataset from Qatar University which contains 3379 cases for light, normal and heavy COVID-19 lung infection. The CT dataset contains the scans of 10 patient cases with COVID-19, a total of 1562 CT axial slices. The BAA dataset is obtained from the hospital and includes 387 original images. The ISIC 2018 dataset is from the International Skin Imaging Collaborative (ISIC) containing 2594 original images.Results: The proposed MS-DCANet achieved evaluation metrics (MIOU) of 73.86, 97.26, 89.54, and 79.54 on the four datasets, respectively, far exceeding other current state-of-the art baselines.Conclusion: The proposed MS-DCANet can help clinicians to automate the diagnosis of COVID-19 patients with different symptoms.Keywords: multi-modality COVID-19 lesion segmentations, laboratory to clinic, multi-scale feature learning, depthwise separable convolution

Keywords

• multi-modality covid-19 lesion segmentations
• laboratory to clinic
• multi-scale feature learning
• depthwise separable convolution