An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism

• URL: http://arxiv.org/abs/2004.06673v4
• Date: Mon, 8 Feb 2021 13:16:08 GMT
• Title: An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism
• Authors: Tongxue Zhou, St\'ephane Canu, Su Ruan
• Abstract summary: coronavirus disease (COVID-19) pandemic has led to a devastating effect on the global public health. It is of great importance to rapidly and accurately segment COVID-19 from CT to help diagnostic and patient monitoring. We propose a U-Net based segmentation network using attention mechanism.
• Score: 4.4259821861544
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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