Wu Xiaoxin, Zhang Zhihao, Guo Lingling, Chen Hui, Luo Qiaojie, Jin Bei, Gu Weiyan, Lu Fangfang, Chen Jingjing
State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang China.
College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, China.
J Real Time Image Process. 2022;19(6):1091-1104. doi: 10.1007/s11554-022-01249-5. Epub 2022 Sep 4.
The novel coronavirus pneumonia (COVID-19) is the world's most serious public health crisis, posing a serious threat to public health. In clinical practice, automatic segmentation of the lesion from computed tomography (CT) images using deep learning methods provides an promising tool for identifying and diagnosing COVID-19. To improve the accuracy of image segmentation, an attention mechanism is adopted to highlight important features. However, existing attention methods are of weak performance or negative impact to the accuracy of convolutional neural networks (CNNs) due to various reasons (e.g. low contrast of the boundary between the lesion and the surrounding, the image noise). To address this issue, we propose a novel focal attention module (FAM) for lesion segmentation of CT images. FAM contains a channel attention module and a spatial attention module. In the spatial attention module, it first generates rough spatial attention, a shape prior of the lesion region obtained from the CT image using median filtering and distance transformation. The rough spatial attention is then input into two 7 × 7 convolution layers for correction, achieving refined spatial attention on the lesion region. FAM is individually integrated with six state-of-the-art segmentation networks (e.g. UNet, DeepLabV3+, etc.), and then we validated these six combinations on the public dataset including COVID-19 CT images. The results show that FAM improve the Dice Similarity Coefficient (DSC) of CNNs by 2%, and reduced the number of false negatives (FN) and false positives (FP) up to 17.6%, which are significantly higher than that using other attention modules such as CBAM and SENet. Furthermore, FAM significantly improve the convergence speed of the model training and achieve better real-time performance. The codes are available at GitHub (https://github.com/RobotvisionLab/FAM.git).
新型冠状病毒肺炎（COVID-19）是全球最严重的公共卫生危机，对公众健康构成严重威胁。在临床实践中，使用深度学习方法从计算机断层扫描（CT）图像中自动分割病变为识别和诊断COVID-19提供了一个有前景的工具。为了提高图像分割的准确性，采用了注意力机制来突出重要特征。然而，由于各种原因（如病变与周围边界对比度低、图像噪声等），现有的注意力方法对卷积神经网络（CNN）的性能提升较弱或有负面影响。为了解决这个问题，我们提出了一种用于CT图像病变分割的新型焦点注意力模块（FAM）。FAM包含一个通道注意力模块和一个空间注意力模块。在空间注意力模块中，它首先生成粗略的空间注意力，即使用中值滤波和距离变换从CT图像中获得的病变区域的形状先验。然后将粗略的空间注意力输入到两个7×7卷积层进行校正，从而在病变区域实现精细的空间注意力。FAM分别与六个最先进的分割网络（如UNet、DeepLabV3+等）集成，然后我们在包括COVID-19 CT图像的公共数据集上对这六种组合进行了验证。结果表明，FAM将CNN的骰子相似系数（DSC）提高了2%，并将假阴性（FN）和假阳性（FP）的数量减少了多达17.6%，显著高于使用CBAM和SENet等其他注意力模块的情况。此外，FAM显著提高了模型训练的收敛速度，并实现了更好的实时性能。代码可在GitHub（https://github.com/RobotvisionLab/FAM.git）上获取。
