An automatic COVID-19 CT segmentation based on U-Net with attention mechanism
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The coronavirus disease (COVID-19) pandemic has led a devastating effect on the global public health. Computed Tomography (CT) is an effective tool in the screening of COVID-19. It is of great importance to rapidly and accurately segment COVID-19 from CT to help diagnostic and patient monitoring. In this paper, we propose a U-Net based segmentation network using attention mechanism. As not all the features extracted from the encoders are useful for segmentation, we propose to incorporate an attention mechanism to a U-Net architecture to capture rich contextual relationships for better feature representations. In addition, the focal tversky loss is introduced to deal with small lesion segmentation. The experiment results, evaluated on a small dataset where only 100 CT slices are available, demonstrate the proposed method can achieve an accurate and rapid segmentation on COVID-19 segmentation. The obtained Dice Score, Sensitivity and Specificity are 69.1 respectively.
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