  The coronavirus disease (COVID-19) pandemic has led to 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 including a
spatial and a channel attention, to a U-Net architecture to re-weight the
feature representation spatially and channel-wise to capture rich contextual
relationships for better feature representation. In addition, the focal tversky
loss is introduced to deal with small lesion segmentation. The experiment
results, evaluated on a COVID-19 CT segmentation dataset where 473 CT slices
are available, demonstrate the proposed method can achieve an accurate and
rapid segmentation on COVID-19 segmentation. The method takes only 0.29 second
to segment a single CT slice. The obtained Dice Score, Sensitivity and
Specificity are 83.1%, 86.7% and 99.3%, respectively.
