基于深度学习的哮喘患者CT影像黏液栓自动识别

doi:10.13190/j.jbupt.2022-030

北京邮电大学学报 ›› 2022, Vol. 45 ›› Issue (4): 58-63.doi: 10.13190/j.jbupt.2022-030

基于深度学习的哮喘患者CT影像黏液栓自动识别

黄柳婷¹, 刘可欣², 牛凯¹, 常春², 贺志强¹

1. 北京邮电大学泛网无线通信教育部重点实验室, 北京 100876;
2. 北京大学第三医院呼吸与危重症医学科, 北京 100191

收稿日期:2022-02-03 出版日期:2022-08-28 发布日期:2022-06-26
通讯作者: 贺志强(1975—),男,教授,博士生导师,邮箱:hezq@bupt.edu.cn。 E-mail:hezq@bupt.edu.cn
作者简介:黄柳婷(1998—),女,硕士生。
基金资助:
中央高校基本科研业务费专项资金项目（2020XD-A02-1）；国家重点研发计划项目（2021YFE0205300）；首都卫生发展科研专项项目（2020-2-4079）

Automatic Recognition of Mucus Impaction in CT Images of Asthmatic Patients Using Deep Learning

HUANG Liuting¹, LIU Kexin², NIU Kai¹, CHANG Chun², HE Zhiqiang¹

1. Key Laboratory of Universal Wireless Communications (Ministry of Education), Beijing University of Posts and Telecommunications, Beijing 100876, China;
2. Department of Respiratory and Critical Medicine, Peking University Third Hospital, Beijing 100191, China

Received:2022-02-03 Online:2022-08-28 Published:2022-06-26

摘要/Abstract

摘要： 针对肺部计算机断层扫描（CT）影像中人工识别黏液栓效率较低、识别效果不佳等问题，提出一种基于深度神经网络的黏液栓自动识别模型。针对黏液栓不规则的特点，在骨干网络中引入可变形卷积来提取特征，并在检测网络中引入可变形感兴趣区域池化进行特征尺度归一化。针对黏液栓的中小目标特性，提出采用加权特征金字塔网络进行多尺度特征融合。实验结果表明，与传统的更快区域卷积神经网络相比，所提模型的平均精度提升了4%，可为辅助诊断哮喘的严重程度提供参考。

关键词: 黏液栓, 深度学习, 自动识别算法

Abstract: In view of the low efficiency of manual identification of mucus impaction in chest computed tomography (CT) and the poor recognition effect, a deep neural network based automatic recognition algorithm for mucus impaction is proposed. In order to deal with the irregular characteristics of mucus impaction, deformable convolution is added to the backbone to extract features, and deformable region of interest pooling is used in the detection network to normalize the feature scale. Besides, feature pyramid network with weight coefficient is used for multi-scale fusion according to the characteristics of small and medium objects. The results show that compared with the traditional faster region convolutional neural network, mean average precision of the proposed algorithm is improved by 4%, which can provide auxiliary reference for the diagnosis of asthma severity.

Key words: mucus impaction, deep learning, automatic recognition algorithm

中图分类号:

TP183

黄柳婷, 刘可欣, 牛凯, 常春, 贺志强. 基于深度学习的哮喘患者CT影像黏液栓自动识别[J]. 北京邮电大学学报, 2022, 45(4): 58-63.

HUANG Liuting, LIU Kexin, NIU Kai, CHANG Chun, HE Zhiqiang. Automatic Recognition of Mucus Impaction in CT Images of Asthmatic Patients Using Deep Learning[J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(4): 58-63.

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基于深度学习的哮喘患者CT影像黏液栓自动识别

Automatic Recognition of Mucus Impaction in CT Images of Asthmatic Patients Using Deep Learning

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