CT Diagnosis of Chronic Obstructive Pulmonary Disease Based on Slice Correlation Information

Liang Yu-chen; Cai Nian; Ouyang Wen-sheng; Xie Yi-ying; Wang Ping

doi:10.12052/gdutxb.230050

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

CT Diagnosis of Chronic Obstructive Pulmonary Disease Based on Slice Correlation Information

更新时间：2026-05-27

- CT Diagnosis of Chronic Obstructive Pulmonary Disease Based on Slice Correlation Information
- Vol. 41, Issue 1, Pages: 27-33(2024)
- 作者机构：
  
  1. 广东工业大学信息工程学院,广东,广州,510006
  2. 广州医科大学附属第一医院肝胆外科,广东,广州,510120
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.230050
  CLC： TP391
- Online First：25 January 2024，
  
  Published：2024
- 稿件说明：
移动端阅览
梁宇辰, 蔡念, 欧阳文生, 谢依颖, 王平. 基于切片关联信息的慢性阻塞性肺疾病CT诊断[J]. 广东工业大学学报, 2024, 41(1): 27-33.

Liang Yu-chen, Cai Nian, Ouyang Wen-sheng, et al. CT Diagnosis of Chronic Obstructive Pulmonary Disease Based on Slice Correlation Information[J]. 2024, 41(1): 27-33.
梁宇辰, 蔡念, 欧阳文生, 谢依颖, 王平. 基于切片关联信息的慢性阻塞性肺疾病CT诊断[J]. 广东工业大学学报, 2024, 41(1): 27-33. DOI： 10.12052/gdutxb.230050.

Liang Yu-chen, Cai Nian, Ouyang Wen-sheng, et al. CT Diagnosis of Chronic Obstructive Pulmonary Disease Based on Slice Correlation Information[J]. 2024, 41(1): 27-33. DOI： 10.12052/gdutxb.230050.

摘要

慢性阻塞性肺疾病(Chronic Obstructive Pulmonary Disease

COPD) 是一种常见的全球呼吸系统疾病，需要耗费医生大量的时间和精力对CT图像进行初步评估诊断。为了提高阅片效率，提出一种基于CT图像切片关联信息的深度网络，辅助诊断慢性阻塞性肺疾病。提出一种分组方式将网络分成若干个网络分支，每个网络分支能够提取局部CT图像切片内部关联信息，结合双向LSTM技术整合各网络分支信息以提取CT图像全局切片关联信息。为了进一步提升网络分支的局部特征提取能力，融入ConvNeXt提出增强的多头卷积注意力模块。对比实验结果表明，所提出的深度网络能够更好地对CT图像进行分类，辅助COPD诊断，其准确率达到92.15%，敏感度达到94.17%，特异性达到91.17%，AUC达到95.33%。

Abstract

Chronic obstructive pulmonary disease (COPD) is a common respiratory disease of the world

and the doctors need a lot of time to read the abdominal CT images for COPD pre-evaluation. To improve the pre-evaluation efficiency

a deep network based on slice correlation information was proposed for COPD auxiliary diagnosis. First

by using a grouping approach

the architecture of the deep network is divided into several network branches

each of which aims to extract the local intra-slice association information of the CT images. Then

the outputs from multiple network branches are integrated via a BiLSTM to extract the global inter-slice association information between the adjacent CT slices. To further improve the ability of local feature extraction for each network branch

the enhanced multi-headed convolutional attention is designed by embedding the ConvNeXt into the existing multi-headed convolutional attention. Experimental results show that the proposed deep network achieves promising effectiveness for CT image classification on auiliarily diagnose of COPD

and the accuracy

sensitivity

specificity and AUC of the proposed network reach to approximately 92.15%

94.17%

91.17% and 95.33%

respectively.

关键词

Keywords

references

MARKELIĆ I, HLAPČIĆ I, ČERI A, et al . Activation of NLRP3 inflammasome in stable chronic obstructive pulmonary disease [J ] . Scientific Reports, 2022, 12(1): 7544.

VESTBO J, HURD S S, AGUSTÍ A G, et al . Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary [J ] . American Journal of Respiratory and Critical Care Medicine, 2013, 187(4): 347-365.

MATHERS C D, LONCAR D. Projections of global mortality and burden of disease from 2002 to 2030 [J]. PLoS Medicine, 2006, 3(11): e442.

ZHOU Z D. Analysis of clinical treatment in acute exacerbation of chronic obstructive pulmonary disease [J]. China Foreign Medical Treatment, 2020, 39(36): 67-69.

LYNCH D A, AUSTIN J H M, HOGG J C, et al . CT-definable subtypes of chronic obstructive pulmonary disease: a statement of the Fleischner society [J ] . Radiology, 2015, 277(1): 192-205.

GALBÁN C J, HAN M K, BOES J L, et al . Computed tomography-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression [J ] . Nature Medicine, 2012, 18(11): 1711-1715.

LYNCH D A, MOORE C M, WILSON C, et al . CT-based visual classification of emphysema: association with mortality in the COPDGene study [J ] . Radiology, 2018, 288(3): 859-866.

SHAH V, KENIYA R, SHRIDHARANI A, et al . Diagnosis of COVID-19 using CT scan images and deep learning techniques [J ] . Emergency Radiology, 2021, 28(3): 497-505.

POLAT Ö, ŞALK İ, DOĞAN Ö T. Determination of COPD severity from chest CT images using deep transfer learning network [J]. Multimedia Tools and Applications, 2022, 81(15): 21903-21917.

MONDOÑEDO J R, SATO S, OGUMA T, et al . CT imaging-based low-attenuation super clusters in three dimensions and the progression of emphysema [J ] . Chest, 2019, 155(1): 79-87.

XU C, QI S, FENG J, et al . DCT-MIL: deep CNN transferred multiple instance learning for COPD identification using CT images [J ] . Physics in Medicine Biology, 2020, 65(14): 145011.

AHMED J, VESAL S, DURLAK F, et al. COPD classification in CT images using a 3D convolutional neural network[C]// Bildverarbeitung für die Medizin 2020. Berlin: Springer, 2020: 39-45.

VARCHAGALL M, NETHRAVATHI N P, CHANDRAMMA R, et al . Using deep learning techniques to evaluate lung cancer using CT images [J ] . SN Computer Science, 2023, 4(2): 173.

KIENZLE D, LORENZ J, SCHN R, et al. COVID detection and severity prediction with 3D-ConvNeXt and custom pretrainings[C]//Computer Vision–ECCV. Tel Aviv: Springer, 2022: 500-516.

WU Y, DU R, FENG J, et al . Deep CNN for COPD identification by multi-view snapshot integration of 3D airway tree and lung field [J ] . Biomedical Signal Processing and Control, 2023, 79: 104162.

KOLLIAS D, ARSENOS A, SOUKISSIAN L, et al. MIA-COV19D: COVID-19 detection through 3-D chest CT image analysis[C]// International Conference on Computer Vision. Montreal: IEEE/CVF, 2021: 537-544.

HUMPHRIES S M, NOTARY A M, CENTENO J P, et al . Deep learning enables automatic classification of emphysema pattern at CT [J ] . Radiology, 2020, 294(2): 434-444.

LIN H, ZHANG S, LI Q, et al . A new method for heart rate prediction based on LSTM-BiLSTM-Att [J ] . Measurement, 2023, 207: 112384.

FRIEDMAN P J. Imaging studies in emphysema [J]. Proceedings of the American Thoracic Society, 2008, 5(4): 494-500.

LIU Z, MAO H, WU C Y, et al. A convnet for the 2020s[C]//Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE/CVF , 2022: 11976-11986.

WANG W, XIE E, LI X, et al. Pyramid vision transformer: a versatile backbone for dense prediction without convolutions[C]// International Conference on Computer Vision. Montreal: IEEE/CVF, 2021: 568-578.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Dynamic Adaptive Enhancement for Power Scene Images DA-IOD Algorithm for Quantitative Target Detection

Deep Subdomain Adaptation Method Based on Domain Invariant Discriminant Feature Learning

A Hotspot Detector Based on Active Learning and Visual State Space Models

Bimodal Iterative Cross-attention Fusion Ensemble Framework

SCA-YOLO: Real-time Target Detection Based on Deformable Convolution and Context-aware Attention

Related Author

Xu Fangyuan

Chen Hailin

Yuan Haoliang

Chen Jianhua

Hoon Heo

Feng Yongjin

Wu Xiaoling

Xiao Kaixiang

Related Institution

School of Automation, Guangdong University of Technology

Samsung Electro-mechanics Co., Ltd., Suwon City

School of Computer Science and Technology, Guangdong University of Technology

School of Integrated Circuits, Guangdong University of Technology

Faculty of Applied Sciences, Macao Polytechnic University, Macao

AI问答

⁰