No abstract

Automatic segmentation of lung lesions from COVID-19 Computed Tomography (CT) images can help to establish a quantitative model for diagnosis and treatment. For this reason, this work provides a new segmentation method to meet the needs of CT images processing under COVID-19 epidemic. The main steps are as follows: Firstly, the proposed Region of Interest (ROI) extraction implements patch mechanism strategy to satisfy the applicability of 3D network and remove irrelevant background. Secondly, 3D network is established to extract spatial features, where 3D Attention model promotes network to enhance target area. Then, to improve convergence of network, a combination loss function is introduced to lead gradient optimization and training direction. Finally, data augmentation and Conditional Random Field (CRF) are applied to realize data resampling and binary segmentation. This method was assessed with some comparative Experiment. By comparison, the proposed method reached the highest performance. Therefore, it has potential clinical applications.

No abstract

No abstract

No abstract