Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (6): 2164-2173.doi: 10.13229/j.cnki.jdxbgxb20200674

Previous Articles    

COVID⁃19 chest CT image segmentation based on federated learning and blockchain

Sheng-sheng WANG(),Jing-yu CHEN,Yi-nan LU()   

  1. College of Computer Science and Technology，Jilin University，Changchun 130012，China
  • Received:2020-09-03 Online:2021-11-01 Published:2021-11-15
  • Contact: Yi-nan LU E-mail:wss@jlu.edu.cn;luyn@jlu.edu.cn

RICH HTML

 33   

PDF (PC)

857

Abstract:

This paper proposed a COVID-19 chest CT image segmentation method based on Federated Learning (FL) and blockchain to automatically segment the area in lung affected by COVID-19. Firstly, in the situation where the sample data of patients is limited and it is distributed in different institutions, which cannot be easily collected, we applied FL method. Then, we used blockchain network to replace the central server in FL to solve the “single point of failure” problem. Finally, we designed a lightweight separable convolution U-NET to reduce the cost of computation and time. Experimental results show that the method has good performance after training, and its dice metric can achieve 63.26%, which is helpful for diagnosis of COVID-19.

Key words: computer application technology, blockchain, federated learning, Corona Virus Disease 2019, image segmentation

CLC Number: 

  • TP39

Fig.1

Traditional framework of federated learning"

Fig.2

One iteration process for BCFL"

Fig.3

Schematic diagram of bifurcation"

Fig.4

Architecture of separable convolution U-Net"

Fig.5

Two samples of the COVID-19 chest CTsegmentation dataset"

Fig.6

Distribution of training samples（N=360） in local device（ND=5）"

Table 1

Testing results"

方法DiceSensitivitySpecificity
U-Net集中训练65.5869.2194.34
联邦学习62.4765.8192.67
BCFL63.2665.6693.14

Fig.7

Learning curves for three methods"

Fig.8

Partial prediction results"

Fig.9

Relationship between the learning completionlatency and block generation rate λ"

Fig.10

Learning completion latency in thecase of miners' malfunction"

1 Bedford J, Enria D, Giesecke J, et al. COVID-19: towards controlling of a pandemic[J]. The Lancet, 2020, 395(10229): 1015-1018.
2 国家卫生健康委办公厅. 新型冠状病毒肺炎诊疗方案(试行第七版)[J].传染病信息,2020,33(1):1-6, 26.
General office of the national health commission. COVID-19 diagnosis and treatment protocol (trial version 7)[J]. Infectious Disease Information, 2020, 33(1): 1-6, 26.
3 Li Yan, Xia Li-ming. Coronavirus disease 2019 (COVID-19): role of chest CT in diagnosis and management[J]. American Journal of Roentgenology, 2020, 214(6): 1280-1286.
4 管汉雄, 熊颖, 申楠茜, 等. 新型冠状病毒肺炎 (COVID-19)临床影像学特征[J]. 放射学实践, 2020, 35(2): 125-130.
Guan Han-xiong, Xiong Ying, Shen Nan-qian, et al. Clinical and thin-section CT features of patients with the COVID-19 in Wuhan[J]. Radiologic Practice, 2020, 35(2): 125-130.
5 Chung M, Bernheim A, Mei X Y, et al. CT imaging features of 2019 novel coronavirus (2019-nCoV)[J]. Radiology, 2020, 295(1): 202-207.
6 Shi H Y, Han X Y, Jiang N C, et al. Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study[J]. The Lancet Infectious Diseases, 2020, 20(4): 425-434.
7 李欢,程尼涛,孙文博,等. 人工智能辅助定量分析新型冠状病毒肺炎的CT进展类型[J]. 武汉大学学报:医学版, 2021, 42(1):1-5.
Li Huan, Cheng Ni-tao, Sun Wen-bo, et al. AI-assisted quantitative analysis of chest CT progression patterns of COVID-19[J]. Medical Journal of Wuhan University, 2021, 42(1):1-5.
8 Sheller M J, Reina G A, Edwards B, et al. Multi-institutional deep learning modeling without sharing patient data: a feasibility study on brain tumor segmentation[C]∥International MICCAI Brainlesion Workshop. Cham: Springer, 2018: 92-104.
9 Nakamoto S. Bitcoin: a peer-to-peer electronic cash system[J/OL]. [2020-08-23].
10 袁勇,王飞跃. 区块链技术发展现状与展望[J]. 自动化学报, 2016, 42(4):481-494.
Yuan Yong, Wang Fei-yue. Blockchain: the state of the art and future trends[J]. Acta Automatica Sinica, 2016, 42(4):481-494.
11 Konečný J, McMahan H B, Ramage D, et al. Federated optimization: distributed machine learning for on-device intelligence[J/OL]. [2020-08-10].
12 Yang Q, Liu Y, Chen T J, et al. Federated machine learning: concept and applications[J]. ACM Transactions on Intelligent Systems and Technology, 2019, 10(2):No.12.
13 杨强. AI与数据隐私保护:“联邦学习”的破解之道[J]. 信息安全研究, 2019, 5(11):961-965.
Yang Qiang. AI and data privacy protection:the way to federated learning[J]. Journal of Information Security Research, 2019, 5(11):961-965.
14 王赫彬. 区块链技术与应用前瞻综述[J].科技创新导报, 2020, 17(14):126-127.
Wang He-bin. Overview of blockchain technology and application prospects[J]. Science and Technology Innovation Herald, 2020, 17(14): 126-127.
15 Rahman M U, Guidi B, Baiardi F. Blockchain-based access control management for decentralized online social networks[J]. Journal of Parallel and Distributed Computing, 2020, 144: 41-54.
16 Yue X, Wang H J, Jin D W, et al. Healthcare data gateways: found healthcare intelligence on blockchain with novel privacy risk control[J]. Journal of Medical Systems, 2016, 40(10):No.218.
17 Zheng Z B, Xie S A, Dai H N, et al. An overview of blockchain technology: architecture, consensus, and future trends[C]∥2017 IEEE International Congress on Big Data, Honolulu, HI, USA, 2017: 557-564.
18 肖明尧, 李雄飞, 张小利, 等. 基于多尺度的区域生长的图像分割算法[J]. 吉林大学学报:工学版, 2017, 47(5): 1591-1597.
Xiao Ming-yao, Li Xiong-fei, Zhang Xiao-li, et al. Medical image segmentation algorithm based on multi-scale region growing[J]. Journal of Jilin University(Engineering and Technology Edition), 2017, 47(5): 1591-1597.
19 Al-Amri S S, Kalyankar N V, Khamitkar S D. Image segmentation by using edge detection[J]. International Journal on Computer Science and Engineering, 2010, 2(3): 804-807.
20 Badrinarayanan V, Kendall A, Cipolla R. Segnet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12): 2481-2495.
21 Kumar A, Upadhyay N, Ghosal P, et al. CSNet: a new deepnet framework for ischemic stroke lesion segmentation[J]. Computer Methods and Programs in Biomedicine, 2020,193: No.105524.
22 Zhou Z X, He Z S, Jia Y Y. AFPNet: a 3D fully convolutional neural network with atrous-convolution feature pyramid for brain tumor segmentation via mri images[J]. Neurocomputing, 2020, 402: 235-244.
23 郜峰利,陶敏,李雪妍,等. 基于深度学习的CT影像脑卒中精准分割[J]. 吉林大学学报:工学版, 2020, 50(2): 678-684.
Gao Feng-li, Tao Min, Li Xue-yan, et al. Accurate segmentation of stroke in CT image based on deep learning[J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(2): 678-684.
24 Samarakoon S, Bennis M, Saad W, et al. Distributed federated learning for ultra-reliable low-latency vehicular communications[J]. IEEE Transactions on Communications, 2019, 68(2): 1146-1159.
25 朱辉,秦品乐. 基于多尺度特征结构的U-Net肺结节检测算法[J]. 计算机工程, 2019, 45(4): 254-261.
Zhu Hui,Qin Pin-le. U-Net pulmonary nodule detection algorithm based on multi-scale feature structure[J]. Computer Engineering, 2019, 45(4): 254-261.
26 魏柳,向智霆,刘剑聪,等.基于回环残差注意力机制U-net的胰腺分割[J].重庆邮电大学学报:自然科学版, 2021, 33(4): 653-660.
Wei Liu, Xiang Zhi-ting, Liu Jian-cong,et al. Pancreas segmentation based on ringed residual attention U-net[J]. Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition), 2021, 33(4): 653-660.
27 郝华颖, 赵昆, 苏攀, 等. 一种基于改进ResU-Net的角膜神经分割算法[J]. 计算机工程, 2021, 47(1): 217-223.
Hao Hua-ying, Zhao Kun, Su Pan, et al. A corneal nerve segmentation algorithm based on improved ResU-Net[J]. Computer Engineering, 2021, 47(1): 217-223.
28 Decker C, Wattenhofer R. Information propagation in the bitcoin network[C]∥IEEE P2P 2013 Proceedings, Trento, Italy, 2013: 1-10.
29 Rodrigues P. COVID-19 CT segmentation dataset[DB/OL]. [2020-08-30].
[1] Jie CAO,Xue QU,Xiao-xu LI. Few⁃shot image classification method based on sliding feature vectors [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1785-1791.
[2] Hong-wei ZHAO,Zi-jian ZHANG,Jiao LI,Yuan ZHANG,Huang-shui HU,Xue-bai ZANG. Bi⁃direction segmented anti⁃collision algorithm based on query tree [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1830-1837.
[3] Chun-bo WANG,Xiao-qiang DI. Cloud storage integrity verification audit scheme based on label classification [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1364-1369.
[4] Rong QIAN,Ru ZHANG,Ke-jun ZHANG,Xin JIN,Shi-liang GE,Sheng JIANG. Capsule graph neural network based on global and local features fusion [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 1048-1054.
[5] Shu-tao SHEN,Zha-xi NIMA. Double chaos identifiable tampering image encryption method based on blockchain technology [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 1055-1059.
[6] Qian-yi XU,Gui-he QIN,Ming-hui SUN,Cheng-xun MENG. Classification of drivers' head status based on improved ResNeSt [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 704-711.
[7] Lu-shen WU,Wei CHENG,Yun HU. Image segmentation of multilevel threshold based on improved cuckoo search algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(1): 358-369.
[8] Yuan SONG,Dan-yuan ZHOU,Wen-chang SHI. Method to enhance security function of OpenStack Swift cloud storage system [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(1): 314-322.
[9] Xiang-jiu CHE,You-zheng DONG. Improved image recognition algorithm based on multi⁃scale information fusion [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1747-1754.
[10] Feng-li GAO,Min TAO,Xue-yan LI,Xin HE,Fan YANG,Zhuo WANG,Jun-feng SONG,Dan TONG. Accurate segmentation of stroke in CT image based on deep learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(2): 678-684.
[11] Jun-jun LI,Jian-nong CAO,Bei-bei CHENG,Juan LIAO,Ying-ying ZHU. High spatial resolution remote sensing imagery segmentation based on combination of pixels and multi⁃scaleobjects using spectral clustering [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 2098-2108.
[12] LIU Zhong-min,WANG Yang,LI Zhan-ming,HU Wen-jin. Image segmentation algorithm based on SLIC and fast nearest neighbor region merging [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1931-1937.
[13] XIAO Ming-yao, LI Xiong-fei, ZHANG Xiao-li, ZHANG Liu. Medical image segmentation algorithm based on multi-scale region growing [J]. 吉林大学学报(工学版), 2017, 47(5): 1591-1597.
[14] LIU Zhong-min, LI Zhan-ming, LI Bo-hao, HU Wen-jin. Spectral clustering image segmentation based on sparse matrix [J]. 吉林大学学报(工学版), 2017, 47(4): 1308-1313.
[15] ZHAO Fu-qun, ZHOU Ming-quan, GENG Guo-hua. Image threshold segmentation with GA-Otsu method and quantitative identification [J]. 吉林大学学报(工学版), 2017, 47(3): 959-964.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd