Abstract
Automatic image segmentation is critical for medical image segmentation. For example, automatic segmentation of infection area of COVID-19 before and after diagnosis and treatment can help us automatically analyze the diagnosis and treatment effect. The existing algorithms do not solve the problems of insufficient data and insufficient feature extraction at the same time. In this paper, we propose a new data augmentation algorithm to handle the insufficient data problem, named Joint Mix; we utilize an improved U-Net with context encoder to enhance the feature extraction ability. Experiments in the segmentation of COVID-19 infection region using CT images demonstrate its effectiveness.
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