基于切片关联信息的慢性阻塞性肺疾病CT诊断

梁宇辰; 蔡念; 欧阳文生; 谢依颖; 王平

doi:10.12052/gdutxb.230050

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基于切片关联信息的慢性阻塞性肺疾病CT诊断

智慧医疗 | 更新时间：2026-05-27

- 基于切片关联信息的慢性阻塞性肺疾病CT诊断
- CT Diagnosis of Chronic Obstructive Pulmonary Disease Based on Slice Correlation Information
- 广东工业大学学报 2024年41卷第1期页码：27-33
- 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：
  
  梁宇辰 (1998–)，男，硕士研究生，主要研究方向为深度学习、医学图像处理等
  蔡念 (1976–)，男，教授，博士生导师，主要研究方向为机器视觉、机器学习、数字信号处理等，E-mail:cainian@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目 (82172019);广州市科技计划项目 (202102010251)
- DOI：10.12052/gdutxb.230050
  中图分类号： TP391
- 网络首发：2024-01-25，
  
  纸质出版：2024
- 稿件说明：
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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

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