论文标题
使用空间和频道注意机制的自动COVID-19 CT分割网络
An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism
论文作者
论文摘要
冠状病毒病（COVID-19）大流行导致对全球公共卫生的毁灭性影响。计算机断层扫描（CT）是筛选Covid-19的有效工具。快速，准确地将COVID-19从CT划分为诊断和患者监测非常重要。在本文中，我们使用注意机制提出了一个基于U-NET的分割网络。由于并非所有从编码器中提取的功能都对分割有用，因此我们建议将注意机制（包括空间和渠道注意力）纳入U-NET体系结构，以将特征表示在空间和渠道上重新进行重新权重，以捕获丰富的上下文关系，以获得更好的特征表示。此外，引入了局灶性tversky损失来处理小病变细分。实验结果，对可获得473 CT切片的COVID-19 CT分割数据集进行了评估，证明该方法可以实现对COVID-19分段的准确而快速的分割。该方法仅需0.29秒即可分割单个CT切片。获得的骰子得分，灵敏度和特异性分别为83.1％，86.7％和99.3％。
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.
