计算机与现代化 ›› 2023, Vol. 0 ›› Issue (06): 21-26.doi: 10.3969/j.issn.1006-2475.2023.06.004
徐皓1, 田振宇1, 李超凡2, 崔欣欣1, 杨建兰3
收稿日期:
2022-07-19
修回日期:
2022-08-18
出版日期:
2023-06-28
发布日期:
2023-06-28
通讯作者:
杨建兰(1974—),男,福建宁德人,副教授,硕士,研究方向:医学影像识别与应用,E-mail: FJYJL@gszy.edu.cn。
作者简介:
徐皓(1999—),男,江苏泰州人,硕士研究生,研究方向:医学图像处理,E-mail: 1597562722@qq.com; 田振宇(1997—),女,河北张家口人,硕士研究生,研究方向:医院信息化系统,E-mail: tin17472316@163.com; 李超凡(1995—),男,江苏徐州人,硕士,研究方向:医学信息处理,E-mail: lichaofanautism@163.com; 崔欣欣(2000—),女,甘肃天水人,硕士研究生,研究方向:医学影像学,E-mail: taracx0907@163.com。
基金资助:
XU Hao1, TIAN Zhen-yu1, LI Chao-fan2, CUI Xin-xin1, YANG Jian-lan3
Received:
2022-07-19
Revised:
2022-08-18
Online:
2023-06-28
Published:
2023-06-28
摘要: 新型冠状病毒感染早期感染表现为肺浑浊程度和密度增加等特征,为了解决早期患者电子计算机断层扫描(Computed Tomogra, CT)诊断与肺部病灶定位困难这一问题,提出一种ResNeXt和改进型nnU-Net(no-new-Net)的COVID-19(Corona Virus Disease 2019)诊断与肺部病灶分割实验方案。ResNeXt模型分类平均准确率Accuracy为0.8554,AUC面积为0.8951,精确率Precision为0.8321,F1得分为0.8132,改进型nnU-Net模型病灶分割平均Dice系数达到0.7663,相较其他模型分割能力综合提高16.4%。实验结果表明该方案能够增强新冠早期肺部CT图像感染特征提取能力,高效实现疾病分型和精准分割病灶。
中图分类号:
徐皓, 田振宇, 李超凡, 崔欣欣, 杨建兰. 基于ResNeXt和改进nnU-Net的新冠感染早期诊断方法[J]. 计算机与现代化, 2023, 0(06): 21-26.
XU Hao, TIAN Zhen-yu, LI Chao-fan, CUI Xin-xin, YANG Jian-lan. An Early Diagnosis Method of COVID-19 Infection Based on ResNeXt and Improved nnU-Net[J]. Computer and Modernization, 2023, 0(06): 21-26.
[1] | FAN D, ZHOU T, JI G, et al.Inf-Net: Automatic COVID-19 lung infection segmentation from CT images[J]. IEEE Transactions on Medical Imaging, 2020,39(8):2626-2637. |
[2] | MÜLLER D, SOTO-REY I, KRAMER F. Robust chest CT image segmentation of COVID-19 lung infection based on limited data[J]. Informatics in Medicine Unlocked, 2021,25. DOI: 10.1016/j.imu.2021.100681. |
[3] | JIANG Y, CHEN H, LOEW M, et al.COVID-19 CT image synthesis with a conditional generative adversarial network[J]. IEEE Journal of Biomedical and Health Informatics, 2021,25(2):441-452. |
[4] | RAJAMANI K, SIEBERT H, HEINRICH M P.Dynamic deformable attention (DDANet) for semantic segmentation[J]. IEEE Journal of Biomedical and Health Informatics,2021. DOI:10.1101/2020.08.25.20181834. |
[5] | WANG B, JIN S, YAN Q, et al.AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system[J]. Applied Soft Computing, 2020,98. DOI: 10.1016/j.asoc.2020.106897. |
[6] | LV F, WANG J, YU X, et al.Chinese expert consensus on critical care ultrasound applications at COVID-19 pandemic[J]. Advanced Ultrasound in Diagnosis and Therapy, 2020,4(2):27-42. |
[7] | GOZES O, FRID-ADAR M, GREENSPAN H, et al.Rapid AI development cycle for the coronavirus (COVID-19) pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysis[J]. arXiv preprint arXiv:2003.05037, 2020. |
[8] | TIAN S, HU W, NIU L, et al.Pulmonary pathology of early-phase 2019 novel coronavirus (COVID-19) pneumonia in two patients with lung cancer[J]. Journal of Thoracic Oncology, 2020,15(5):700-704. |
[9] | SHEN D G, WU G R, SUK H.Deep learning in medical image analysis[J]. Annual Review of Biomedical Engineering, 2017,19:221-248. |
[10] | JIN C, CHEN W, CAO Y, et al.Development and evaluation of an artificial intelligence system for COVID-19 diagnosis[J]. Nature Communications, 2020,11(1):1-14. |
[11] | RAHIMZADEH M, ATTAR A.A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2[J]. Informatics in Medicine Unlocked, 2020,19. DOI: 10.1016/j.imu.2020.100360. |
[12] | PENG L, WANG C, TIAN G, et al.Analysis of CT scan images for COVID-19 pneumonia based on a deep ensemble framework with DenseNet, Swin transformer, and RegNet[J]. Frontiers in Microbiology, 2022,13. DOI: 10.3389/fmicb.2022.995323. |
[13] | OULEFKI A, AGAIAN S, TRONGTIRAKUL T.Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images[J]. Pattern Recognition, 2021,114. DOI: 10.1016/j.patcog.2020.107747. |
[14] | BENMALEK E, ELMHAMDI J, JILBAB A.Comparing CT scan and chest X-ray imaging for COVID-19 diagnosis[J]. Biomedical Engineering Advances, 2021,1. DOI: 10.1016/j.bea.2021.100003. |
[15] | SENGUPTA A, YE Y, WANG R, et al.Going deeper in spiking neural networks: VGG and residual architectures[J]. Frontiers in Neuroscience, 2019,13. DOI:10.3389/fnins.2019.00095. |
[16] | PANT G, YADAV D P, GAUR A.ResNeXt convolution neural network topology-based deep learning model for identification and classification of Pediastrum[J]. Algal Research, 2020,48. DOI: 10.1016/j.algal.2020.101932. |
[17] | ZHOU T, ZHAO Y, WU J.ResNeXt and Res2Net structures for speaker verification[C]// 2021 IEEE Spoken Language Technology Workshop (SLT). 2021:301-307. |
[18] | MILLETARI F, NAVAB N, AHMADI S A.V-Net: Fully convolutional neural networks for volumetric medical image segmentation[C]// 2016 4th International Conference on 3D Vision (3DV). 2016:565-571. |
[19] | ISENSEE F, JÄGER P F, KOHL S A A, et al. Automated design of deep learning methods for biomedical image segmentation[J]. arXiv preprint arXiv:1904.08128, 2019. |
[20] | ISENSEE F, PETERSEN J, KLEIN A, et al. nnU-Net: Self-adapting framework for U-Net-based medical image segmentation[J]. arXiv preprint arXiv:1809.10486, 2018. |
[21] | 谢娟英,夏琴. 新冠肺炎CXR图像分类新模型COVID-SERA-NeXt[J]. 太原理工大学学报, 2022,53(1):52-62. |
[22] | JAVAHERI T, HOMAYOUNFAR M, AMOOZGAR Z, et al.CovidCTNet: An open-source deep learning approach to identify covid-19 using CT image[J]. arXiv preprint arXiv:2005.03059, 2020. |
[23] | BIZOPOULOS P, VRETOS N, DARAS P.Comprehensive comparison of deep learning models for lung and COVID-19 lesion segmentation in CT scans[J]. arXiv preprint arXiv:2009.06412, 2020. |
[24] | ZHAO R, QIAN B, ZHANG X, et al.Rethinking dice loss for medical image segmentation[C]// 2020 IEEE International Conference on Data Mining (ICDM). 2020:851-860. |
[25] | VEIT A, WILBER M, BELONGIE S.Residual networks behave like ensembles of relatively shallow networks[J]. Proceedings of the 30th International Conference on Neural Information Processing Systems. 2016:550-558. |
[26] | ZHU X, CHENG D, ZHANG Z, et al.An empirical study of spatial attention mechanisms in deep networks[C]// 2019 IEEE/CVF International Conference on Computer Vision (ICCV). 2019:6687-6696. |
[27] | WOO S, PARK J, LEE J, et al.CBAM: Convolutional block attention module[C]// Computer Vision-ECCV 2018. 2018:3-19. |
[28] | TAKIKAWA T, ACUNA D, JAMPANI V, et al.Gated-SCNN: Gated shape CNNs for semantic segmentation[C]// 2019 IEEE/CVF International Conference on Computer Vision (ICCV). 2019:5229-5238. |
[1] | 付鸿林, 张太红, 杨雅婷, 艾孜麦提·艾瓦尼尔, 马 博. 基于生成对抗网络的维语场景文字修改网络[J]. 计算机与现代化, 2024, 0(01): 41-46. |
[2] | 李颖颖, 黄文培. 基于优化八叉树的场景视锥体裁剪算法[J]. 计算机与现代化, 2024, 0(01): 103-108. |
[3] | 马泽宇, 叶 宁, 徐 康, 王 甦, 王汝传, . 基于FMCW雷达和ResNeSt-GRU的行为识别方法[J]. 计算机与现代化, 2023, 0(11): 101-107. |
[4] | 张嘉琪, 徐啟蕾. 基于NAM-YOLO网络的苹果缺陷检测算法[J]. 计算机与现代化, 2023, 0(10): 53-58. |
[5] | 李延满, 王必恒, 赵羚焱. 基于轻量化YOLOv5的安全帽检测[J]. 计算机与现代化, 2023, 0(10): 59-64. |
[6] | 张 楠, 李温静, 刘 彩, 谢 可, 马世乾, 肖钧浩, 邹 枫. 基于多源数据的电力作业人员实时行为安全预警[J]. 计算机与现代化, 2023, 0(10): 84-91. |
[7] | 陈子健, 段春红. 面向在线学习情境的认知情绪面部表情识别[J]. 计算机与现代化, 2023, 0(10): 92-98. |
[8] | 农皓程, 任德均, 任秋霖, 刘澎笠, 黄德成. 基于改进ConvNeXt的软塑包装表面异常检测算法[J]. 计算机与现代化, 2023, 0(08): 12-17. |
[9] | 刘昱杉, 刘卫康, 刘庆华, 者甜甜, 王家晨. 基于YOLOX结合DeepSort的船载车辆行人检测算法[J]. 计算机与现代化, 2023, 0(08): 60-67. |
[10] | 山 雨, 张好鹏, 池 静. 基于改进YOLOv4的轻量化车牌检测算法[J]. 计算机与现代化, 2023, 0(07): 99-104. |
[11] | 罗 伟, 刘思远, 徐健祥, 董天培. 基于改进YOLOv5s的太阳能电池缺陷检测算法[J]. 计算机与现代化, 2023, 0(07): 119-126. |
[12] | 李实秋. 一种基于协作表示的判别局部保持投影方法[J]. 计算机与现代化, 2023, 0(06): 43-47. |
[13] | 彭明康, 冯成德. 基于双目视觉去除附着噪声的改进算法[J]. 计算机与现代化, 2023, 0(06): 62-68. |
[14] | 周芳宇, 陈淑荣. 一种特征增强的Tri-CNN行人再识别方法[J]. 计算机与现代化, 2020, 0(09): 60-65. |
[15] | 李运川, 王晓红, 陈思吉, 葛义攀, 李闯. 基于多核并行和动态阈值的点云配准算法[J]. 计算机与现代化, 2020, 0(09): 77-82. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||