Pei Hong-Yang, Yang Dan, Liu Guo-Ru, Lu Tian
Key Laboratory of Infrared Optoelectric Materials and Micro-Nano DevicesNortheastern University Shenyang 110819 China.
College of Information Science and EngineeringNortheastern University Shenyang 110819 China.
IEEE Access. 2021 Mar 19;9:47144-47153. doi: 10.1109/ACCESS.2021.3067047. eCollection 2021.
The new coronavirus, which has become a global pandemic, has confirmed more than 88 million cases worldwide since the first case was recorded in December 2019, causing over 1.9 million deaths. Since COIVD-19 lesions have clear imaging features on CT images, it is suitable for the auxiliary diagnosis and treatment of COVID-19. Deep learning can be used to segment the lesions areas of COVID-19 in CT images to help monitor the epidemic situation. In this paper, we propose a multi-point supervision network (MPS-Net) for segmentation of COVID-19 lung infection CT image lesions to solve the problem of a variety of lesion shapes and areas. A multi-scale feature extraction structure, a sieve connection structure (SC), a multi-scale input structure and a multi-point supervised training structure were implemented into MPS-Net. In order to increase the ability to segment various lesion areas of different sizes, the multi-scale feature extraction structure and the sieve connection structure will use different sizes of receptive fields to extract feature maps of various scales. The multi-scale input structure is used to minimize the edge loss caused by the convolution process. In order to improve the accuracy of segmentation, we propose a multi-point supervision training structure to extract supervision signals from different up-sampling points on the network. Experimental results showed that the dice similarity coefficient (DSC), sensitivity, specificity and IOU of the segmentation results of our model are 0.8325, 0.8406, 09988 and 0.742, respectively. The experimental results demonstrated that the network proposed in this paper can effectively segment COVID-19 infection on CT images. It can be used to assist the diagnosis and treatment of new coronary pneumonia.
新型冠状病毒已成为全球大流行病，自2019年12月记录首例病例以来，全球确诊病例已超过8800万例，造成超过190万人死亡。由于COVID-19病变在CT图像上具有清晰的影像学特征，适用于COVID-19的辅助诊断和治疗。深度学习可用于分割CT图像中COVID-19的病变区域，以帮助监测疫情。本文提出一种用于分割COVID-19肺部感染CT图像病变的多点监督网络（MPS-Net），以解决病变形状和面积多样的问题。在MPS-Net中实现了多尺度特征提取结构、筛网连接结构（SC）、多尺度输入结构和多点监督训练结构。为了提高分割不同大小的各种病变区域的能力,多尺度特征提取结构和筛网连接结构将使用不同大小的感受野来提取各种尺度的特征图。多尺度输入结构用于最小化卷积过程引起的边缘损失。为了提高分割精度，我们提出一种多点监督训练结构，从网络上不同的上采样点提取监督信号。实验结果表明，我们模型分割结果的骰子相似系数（DSC）、灵敏度、特异性和交并比（IOU）分别为0.8325、0.8406、0.9988和0.742。实验结果表明，本文提出的网络能够有效地分割CT图像上的COVID-19感染。它可用于辅助新型冠状病毒肺炎的诊断和治疗。
