纹理分支网络：基于超声影像的慢性肾脏病筛查模型

郝鹏翼; 徐震宇; 田树元; 吴福理; 陈为; 吴健; 罗笑南

doi:10.1631/FITEE.1900210

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纹理分支网络：基于超声影像的慢性肾脏病筛查模型

常规文章 | Updated：2022-05-19

- 纹理分支网络：基于超声影像的慢性肾脏病筛查模型
- Texture branch network for chronic kidney disease screening based on ultrasound images
- 信息与电子工程前沿（英文） 2020年21卷第8期页码：1161-1170
- Affiliations：
  
  College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
  Tongde Hospital of Zhejiang Province, Hangzhou 310012, China
  The First Affiliated Hospital, Zhejiang University, Hangzhou 310003, China
  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
  Real Doctor AI Research Center, Zhejiang University, Hangzhou 310027, China
  Institute of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China
- Author bio：
  
  [ "", "Fu-li WU, corresponding author of this invited paper, is an associate professor at Zhejiang University of Technology. He received his PhD degree from Zhejiang University, China in 2005. From 2006 to 2007, he was a post-doctor of the University of Bedfordshire. From 2012 to 2013, he was a visiting scholar of the University of California-Davis. His research interests include computer graphics, data visualization, and medical image processing" ]
  [ "", "Prof. Jian WU, received a bachelor's degree and a PhD from College of Computer Science and Technology, Zhejiang University, China. He is the director of Real Doctor AI Research Center of Zhejiang University, member of CCF Youth Working Committee, CCF TCSC, and CCF TCAPP, one of the 151 Talents Program from Zhejiang Province, and a member of the key field innovation team of the Ministry of Science and Technology. His research interests focus on Artificial Intelligence in Medicine, Services Computing, and so on" ]
- Funds：
  
  Project supported by the Zhejiang Provincial Natural Science Foundation of China (No. LY18F020034), the Zhejiang Provincial Medical Health Science and Technology Project, China (No. 2014KYB320), the National Natural Science Foundation of China (Nos. 61801428 and 61672543), the Zhejiang University Education Foundation, China (Nos. K18-511120-004 and K17-511120-017), and the Major Scientific Project of Zhejiang Lab, China (No. 2018DG0ZX01)
- DOI：10.1631/FITEE.1900210
  中图分类号： TP183;TP391.7
- 收稿：2019-04-24，
  
  修回：2019-;8-23，
  
  网络出版：2019-12-12，
  
  纸质出版：2020-08
- Accepted：
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郝鹏翼, 徐震宇, 田树元, 等. 纹理分支网络：基于超声影像的慢性肾脏病筛查模型[J]. 信息与电子工程前沿（英文）, 2020,21(8):1161-1170.

Peng-yi HAO, Zhen-yu XU, Shu-yuan TIAN, et al. Texture branch network for chronic kidney disease screening based on ultrasound images[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(8): 1161-1170.
郝鹏翼, 徐震宇, 田树元, 等. 纹理分支网络：基于超声影像的慢性肾脏病筛查模型[J]. 信息与电子工程前沿（英文）, 2020,21(8):1161-1170. DOI： 10.1631/FITEE.1900210.

Peng-yi HAO, Zhen-yu XU, Shu-yuan TIAN, et al. Texture branch network for chronic kidney disease screening based on ultrasound images[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(8): 1161-1170. DOI： 10.1631/FITEE.1900210.

摘要

慢性肾脏病是一种在世界范围内广泛存在的肾脏疾病。该疾病一旦发展到晚期，伴随而来的是严重并发症与较高死亡风险。因此，早期筛查对于慢性肾脏病诊治至关重要。超声作为一种无创方法，能动态观察肾脏形态和病理特征，常用于肾脏检查。本文提出一种新的卷积神经网络模型，称为纹理分支网络，基于超声影像作慢性肾脏病筛查。该模型通过在经典卷积神经网络中引入纹理分支来提取和优化纹理特征，可自动生成输入图像的纹理特征和深度特征，并使用融合信息进行分类。此外，通过迁移学习训练网络的主干部分，并在具有226张超声影像的数据集上开展实验。实验结果表明，该模型准确率和敏感度分别达到96.01%和99.44%，在慢性肾脏病筛查上具有一定有效性。

Abstract

Chronic kidney disease (CKD) is a widespread renal disease throughout the world. Once it develops to the advanced stage

serious complications and high risk of death will follow. Hence

early screening is crucial for the treatment of CKD. Since ultrasonography has no side effects and enables radiologists to dynamically observe the morphology and pathological features of the kidney

it is commonly used for kidney examination. In this study

we propose a novel convolutional neural network (CNN) framework named the texture branch network to screen CKD based on ultrasound images. This introduces a texture branch into a typical CNN to extract and optimize texture features. The model can automatically generate texture features and deep features from input images

and use the fused information as the basis of classification. Furthermore

we train the base part of the network by means of transfer learning

and conduct experiments on a dataset with 226 ultrasound images. Experimental results demonstrate the effectiveness of the proposed approach

achieving an accuracy of 96.01% and a sensitivity of 99.44%.

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references

UR Acharya , , , KM Meiburger , , , JEW Koh , , , 等 . . Automated detection of chronic kidney disease using higherorder features and elongated quinary patterns from Bmode ultrasound images . . Neur Comput Appl, in press , , 2019 . . DOI: 10.1007/s00521-019-04025-y http://doi.org/10.1007/s00521-019-04025-y . .

M Ahmad , , , V Tundjungsari , , , D Widianti , , , 等 . . Diagnostic decision support system of chronic kidney disease using support vector machine . . Proc 2 nd Int Conf on Informatics and Computing , , 2017 . . p.1 - - 4 . . DOI: 10.1109/IAC.2017.8280576 http://doi.org/10.1109/IAC.2017.8280576 . .

A Batra , , , U Batra , , , V Singh . . A review to predictive methodology to diagnose chronic kidney disease . . Proc 3 rd Int Conf on Computing for Sustainable Global Development , , 2016 . . p.2760 - - 2763 . . . .

EH Chang , , , WK Chong , , , SK Kasoji , , , 等 . . Diagnostic accuracy of contrast-enhanced ultrasound for characterization of kidney lesions in patients with and without chronic kidney disease . . BMC Nephrol , , 2017 . . 18 Article 266 DOI: 10.1186/s12882-017-0681-8 http://doi.org/10.1186/s12882-017-0681-8 . .

A Charleonnan , , , T Fufaung , , , T Niyomwong , , , 等 . . Predictive analytics for chronic kidney disease using machine learning techniques . . Proc Management and Innovation Technology Int Conf , , 2016 . . p.MIT-80 - - MIT-83 . . DOI: 10.1109/MITICON.2016.8025242 http://doi.org/10.1109/MITICON.2016.8025242 . .

H Chen , , , Q Dou , , , D Ni , , , 等 . . Automatic fetal ultrasound standard plane detection using knowledge transferred recurrent neural networks . . Proc 18 th Int Conf on Medical Image Computing and Computer-Assisted Intervention , , 2015 . . p.507 - - 514 . . DOI: 10.1007/978-3-319-24553-9_62 http://doi.org/10.1007/978-3-319-24553-9_62 . .

N Dalal , , , B Triggs . . Histograms of oriented gradients for human detection . . Proc IEEE Computer Society Conf on Computer Vision and Pattern Recognition , , 2005 . . p.886 - - 893 . . DOI: 10.1109/CVPR.2005.177 http://doi.org/10.1109/CVPR.2005.177 . .

K Dhindsa , , , LC Smail , , , M McGrath , , , 等 . . Grading prenatal hydronephrosis from ultrasound imaging using deep convolutional neural networks . . Proc 15 th Conf on Computer and Robot Vision , , 2018 . . p.80 - - 87 . . DOI: 10.1109/CRV.2018.00021 http://doi.org/10.1109/CRV.2018.00021 . .

T Ecder . . Early diagnosis saves lives: focus on patients with chronic kidney disease . . Kidney Int Suppl , , 2013 . . 3 ( ( 4 ): ): 335 - - 336 . . DOI: 10.1038/kisup.2013.70 http://doi.org/10.1038/kisup.2013.70 . .

AM El Nahas , , , AK Bello . . Chronic kidney disease: the global challenge . . Lancet , , 2005 . . 365 ( ( 9456 ): ): 331 - - 340 . . DOI: 10.1016/S0140-6736(05)17789-7 http://doi.org/10.1016/S0140-6736(05)17789-7 . .

RM Haralick , , , K Shanmugam , , , I Dinstein . . Textural features for image classification . . IEEE Trans Syst Man Cybern , , 1973 . . SMC-3 ( ( 6 ): ): 610 - - 621 . . DOI: 10.1109/TSMC.1973.4309314 http://doi.org/10.1109/TSMC.1973.4309314 . .

KM He , , , XY Zhang , , , SQ Ren , , , 等 . . Delving deep into rectifiers: surpassing human-level performance on ImageNet classification . . Proc IEEE Int Conf on Computer Vision , , 2015 . . p.1026 - - 1034 . . DOI: 10.1109/ICCV.2015.123 http://doi.org/10.1109/ICCV.2015.123 . .

KM He , , , XY Zhang , , , SQ Ren , , , 等 . . Deep residual learning for image recognition . . Proc IEEE Conf on Computer Vision and Pattern Recognition , , 2016 . . p.770 - - 778 . . DOI: 10.1109/CVPR.2016.90 http://doi.org/10.1109/CVPR.2016.90 . .

CY Ho , , , TW Pai , , , YC Peng , , , 等 . . Ultrasonography image analysis for detection and classification of chronic kidney disease . . Proc 6 th Int Conf on Complex, Intelligent, and Software Intensive Systems , , 2012 . . p.624 - - 629 . . DOI: 10.1109/CISIS.2012.180 http://doi.org/10.1109/CISIS.2012.180 . .

JW Hsieh , , , CH Lee , , , YC Chen , , , 等 . . Stage classification in chronic kidney disease by ultrasound image . . Proc 29 th Int Conf on Image and Vision Computing New Zealand , , 2014 . . p.271 - - 276 . . DOI: 10.1145/2683405.2683457 http://doi.org/10.1145/2683405.2683457 . .

F Iqbal , , , AS Pallewatte , , , JP Wansapura . . Texture analysis of ultrasound images of chronic kidney disease . . Proc 17 th Int Conf on Advances in ICT for Emerging Regions , , 2017 . . p.1 - - 5 . . DOI: 10.1109/ICTER.2017.8257787 http://doi.org/10.1109/ICTER.2017.8257787 . .

RA Jeewantha , , , MN Halgamuge , , , A Mohammad , , , 等 . . Classification performance analysis in medical science: using kidney disease data . . Proc Int Conf on Big Data Research , , 2017 . . p.1 - - 6 . . DOI: 10.1145/3152723.3152724 http://doi.org/10.1145/3152723.3152724 . .

V Kunwar , , , K Chandel , , , AS Sabitha , , , 等 . . Chronic kidney disease analysis using data mining classification techniques . . Proc 6 th Int Conf on Cloud System and Big Data Engineering , , 2016 . . p.300 - - 305 . . DOI: 10.1109/CONFLUENCE.2016.7508132 http://doi.org/10.1109/CONFLUENCE.2016.7508132 . .

AS Levey , , , KU Eckardt , , , Y Tsukamoto , , , 等 . . Definition and classification of chronic kidney disease: a position statement from kidney disease: improving global outcomes (KDIGO) . . Kidney Int , , 2005 . . 67 ( ( 6 ): ): 2089 - - 2100 . . DOI: 10.1111/j.1523-1755.2005.00365.x http://doi.org/10.1111/j.1523-1755.2005.00365.x . .

V Lpez , , , A Fernndez , , , S Garca , , , 等 . . An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics . . Inform Sci , , 2013 . . 250 113 - - 141 . . DOI: 10.1016/j.ins.2013.07.007 http://doi.org/10.1016/j.ins.2013.07.007 . .

M Oquab , , , L Bottou , , , I Laptev , , , 等 . . Learning and transferring mid-level image representations using convolutional neural networks . . Proc IEEE Conf on Computer Vision and Pattern Recognition , , 2014 . . p.1717 - - 1724 . . DOI: 10.1109/CVPR.2014.222 http://doi.org/10.1109/CVPR.2014.222 . .

RM Pujari , , , VD Hajare . . Analysis of ultrasound images for identification of chronic kidney disease stages . . Proc 1 st Int Conf on Networks & Soft Computing , , 2014 . . p.380 - - 383 . . DOI: 10.1109/CNSC.2014.6906704 http://doi.org/10.1109/CNSC.2014.6906704 . .

K Sharma , , , J Virmani . . Classification of renal diseases using first order and higher order statistics . . Proc 3 rd Int Conf on Computing for Sustainable Global Development , , 2016 . . p.425 - - 430 . . . .

W Shen , , , M Zhou , , , F Yang , , , 等 . . Multi-scale convolutional neural networks for lung nodule classification . . Proc 24 th Int Conf on Information Processing in Medical Imaging , , 2015 . . p.588 - - 599 . . DOI: 10.1007/978-3-319-19992-4_46 http://doi.org/10.1007/978-3-319-19992-4_46 . .

HC Shin , , , HR Roth , , , MC Gao , , , 等 . . Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning . . IEEE Trans Med Imag , , 2016 . . 35 ( ( 5 ): ): 1285 - - 1298 . . DOI: 10.1109/TMI.2016.2528162 http://doi.org/10.1109/TMI.2016.2528162 . .

MB Subramanya , , , V Kumar , , , S Mukherjee , , , 等 . . SVMbased CAC system for B-mode kidney ultrasound images . . J Dig Imag , , 2015 . . 28 ( ( 4 ): ): 448 - - 458 . . DOI: 10.1007/s10278-014-9754-4 http://doi.org/10.1007/s10278-014-9754-4 . .

A Telea . . An image inpainting technique based on the fast marching method . . J Dig Imag , , 2004 . . 9 ( ( 1 ): ): 23 - - 34 . . DOI: 10.1080/10867651.2004.10487596 http://doi.org/10.1080/10867651.2004.10487596 . .

Q Zheng , , , SL Furth , , , GE Tasian , , , 等 . . Computeraided diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data by integrating texture image features and deep transfer learning image features . . J Pediatr Urol , , 2019 . . 15 ( ( 1 ): ): 75.e1 - - 75.e7 . . DOI: 10.1016/j.jpurol.2018.10.020 http://doi.org/10.1016/j.jpurol.2018.10.020 . .

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