一种辅助新冠肺炎检测的肺实质分割算法

doi:10.19678/j.issn.1000-3428.0060059

摘要/Abstract

摘要： 新冠肺炎给人民生命健康及社会经济带来了巨大冲击，X 光胸片中的肺实质部分对肺炎诊断具有重要的指导意义，因此肺实质分割成为临床诊断肺炎的迫切需求。本文改进了一种编解码模式的图像分割算法，该算法主要针对组织结构复杂的人体肺部结构分割，采用了 A 形特征融合模块（A-block）,有效减少了数据传输过程中的信息丢失。针对分割目标形变较大问题，向网络中加入了改进的可变形卷积。同时，提出了一种类距惩罚分割损失函数，使二分类预测结果更倾向于两极分化。通过实验表明，本文算法的准确率、相似度系数、敏感度、Jaccard 指数分别为 98.16%、98.32%、98.13%、98.54%，均优于其他对比算法，能有效实现肺实质分割任务。

Abstract: The COVID-19 has brought great impact on people's health and social economy. The lung parenchyma in X-ray chest film has important guiding significance for the diagnosis of the COVID-19. In this paper, an image segmentation algorithm based on encoding and decoding mode is improved. The algorithm is mainly aimed at the segmentation of human lung structure with complex tissue structure. A-block is used to reduce the loss of information in the process of data transmission. To solve the problem of large deformation of segmented objects, an improved deformable convolution is added to the network. At the same time, a class distance penalty partition loss function is proposed to make the binary classification prediction results more polarized. The experimental results show that the accuracy, similarity coefficient, sensitivity and Jaccard index of this algorithm are 98.16%, 98.32%, 98.13% and 98.54% respectively, which are better than other comparative algorithms, and can effectively achieve the task of lung parenchyma segmentation.

苏赋, 但涛, 方东. 一种辅助新冠肺炎检测的肺实质分割算法[J]. 计算机工程, doi: 10.19678/j.issn.1000-3428.0060059.

SU Fu, DAN Tao, FANG Dong. A lung parenchyma segmentation algorithm for the COVID-19 detection[J]. Computer Engineering, doi: 10.19678/j.issn.1000-3428.0060059.

参考文献

[1] WAND Niu-niu，AN Jian-cheng. Combining the optimal threshold method and the improved Freeman chain code for lung parenchyma segmentation[J].Computer Engineering & Scien-ce, 2020,42(06):1037-1042. 王妞妞,安建成.最佳阈值法结合改进的 Freeman 链码的肺实质分割 [J]. 计算机工程与科学 , 2020,42(06):1037-1042.
[2] Ronneberger O, Fischer P, Brox T, et al. U-Net: convolutional networks for biomedical image segmentation[C]//Proceedingsof the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Oct 5-9, 2015. Cham: Springer, 2015: 234-241
[3] Milletari F, Navab N, Ahmadi S A. V- Net: fully convolutionalneural networks for volumetric medical image segmentation[C]//Proceedings of the 4th International Conferenceon 3D Vision, Stanford, Oct 25-28, 2016. Washington: IEEE Computer Society, 2016: 565-571.
[4] ZHANG Qianwen , CHEN Ming , QIN Yufang, CHEN Xi. Lung nodule segmentation based on 3D ResUnet network[J]. Chinese Journal of Medical Physics ,2019,36(11):1356-1361. 张倩雯,陈明,秦玉芳,陈希.基于 3D ResUnet 网络的肺结节分割 [J]. 中国医学物理学杂志,2019,36(11):1356-1361.
[5] WANG Pan, QIANG Yan, Yang Xiao-tang, HOU Teng-xuan. Double Attention 3D-UNet for Lung Nodule Segmentation[J/OL]. Computer Engin-eering:1-10[2020-12-18].https://doi.org/10.196-78/j.issn.1000-3428.0057019. 王磐,强彦,杨晓棠,侯腾璇.双注意力 3D-UNet 肺结节分割网络 [J/OL]. 计算机工程 :1-10[2020-12-18]. https://doi.org/10.19678/j.issn.-1000-3428.0057019.
[6] GAO Zhiyong ，HUANG Jinzhen，DU Chenggang. Pulmonary nodule detection based on feature pyramid networks.[J]. Journal of Computer Applications, 2020,46-(03):267-272+279. 高智勇,黄金镇,杜程刚.基于特征金字塔网络的肺结节检测[J].计算机应用,2020,40 (09):25-71-2576.
[7] ZHANG Huahai , BAI Peirui , GUO Ziyang , DU Linghao , LI Chang , REN Yande , YANG Kai , LIU Qingyi. An algorithm for three-dimensional plumonary parenchymal segmentation by integ-rating surfacelet transform with pulse coupled neural network[J]. Journal of Biomedical Engi-neering,2020,37(04):630-640. 张华海,白培瑞,郭子杨,杜令豪,李昶,任延德,杨凯,刘庆一.一种融合表面波变换与脉冲耦合神经网络的三维肺实质分割算法 [J]. 生物医学工程学杂志,2020,37(04):630-640.
[8] QIAN B X, XIAO Z Y, SONG W. Application of improved convolutional neural network in lung image segmentation[J]. Journal of Frontiers of Computer Science and Technology, 2020, 14(8): 1358-1367. 钱宝鑫,肖志勇,宋威.改进的卷积神经网络在肺部图像上的分割应用 [J]. 计算机科学与探索,2020,14(08):1358-1367.
[9] Gu Z , Cheng J , Fu H , et al. CE-Net: Context Encoder Network for 2D Medical Image Segmentation[J]. IEEE Transactions on Medical Imaging, 2019:1-1.
[10] DAI J F, QI H Z XIONG Y W. Deformable convolutional networks [C]// Proceedings of the IEEE International Conference on Computer Vision. Venice Italy: IEEE, 2017: 764-773
[11] Qibin Hou, Ming-Ming Cheng, Xiaowei Hu, Ali Borji, Zhuowen Tu, and Philip Torr. Deeply supervised salient object detection with short connections. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5300–5309, 2017.
[12] Yifan Sun, Changmao Cheng, Yuhan Zhang, Chi Zhang, Liang Zheng, Zhongdao Wang, Yichen Wei, et al. Circle Loss: A Unified Perspective of Pair Similarity Optimization[C]// arXiv, 2020.
[13] Z.W. Zhou, M.M.R. Siddiquee, N. Tajbakhsh and J.M. Liang,“UNet++: A Nested U-Net Architect-ure for Medical Image Segmentation,” Deep Learning in Medical Image Anylysis and Multimodal Learning for Clinical Decision Support, pp: 3-11,2018.
[14] Gu Z , Cheng J , Fu H , et al. CE-Net: Context Encoder Network for 2D Medical Image Segmentation[J]. IEEE Transactions on Medical Imaging, 2019:1-1.
[15] Qin X , Zhang Z , Huang C , et al. U 2 -Net: Going Deeper with Nested U-Structure for Salient Object Detection[J]. Pattern Recognition, 2020, 106:107404.
[16] Huang H , Lin L , Tong R , et al. UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation[C]// arXiv, 2020.
[17] J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao,C.-Y. Cheng, T.Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE Transactions on Medical Imaging, vol. 32, no. 6, pp. 1019–1032, 2013
[18] Geng L, Zhang S Q, Tong J, et al. Lung segmentation method with dilated convolution based on VGG-16 network[J]. Computer Assisted Surgery, 2019, 24: 27-33.
[19] WU Yuchao , LIN Lan , WANG Jingxuan , WU Shuicai. Application of semantic segmentation based on convolutional neural network in medical images[J]. Journal of Biomedical Engineering, 2020,37(03):533-540. 吴玉超,林岚,王婧璇,吴水才.基于卷积神经网络的语义分割在医学图像中的应用[J].生物医学工程学杂志,2020,37(03):533-540.
[20] Shuchao Pang, Zhezhou Yu, and Mehmet A. Orgun. A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images. Computer Methods and Programs in Biomedicine, 140:283–293, 2017. doi:10.1016/J.CMPB.201-6.12.019.
[21] L-C. Chen, Y.K. Zhu, G. Papandreou and H. Adam, “Encoder-decoder with atrous separable convolution for semantic imagesegmentation,” Proceedings of the European Conference on Computer Vision, 2018.