张文静, 李文秀, 刘爱军, 武兴坤, 李剑峰, 罗涛. 基于切面识别的房间隔缺损智能辅助诊断[J]. 工程科学学报, 2021, 43(9): 1166-1173. DOI: 10.13374/j.issn2095-9389.2021.01.14.007
引用本文: 张文静, 李文秀, 刘爱军, 武兴坤, 李剑峰, 罗涛. 基于切面识别的房间隔缺损智能辅助诊断[J]. 工程科学学报, 2021, 43(9): 1166-1173. DOI: 10.13374/j.issn2095-9389.2021.01.14.007
ZHANG Wen-jing, LI Wen-xiu, LIU Ai-jun, WU Xing-kun, LI Jian-feng, LUO Tao. Intelligent auxiliary diagnosis of atrial septal defect based on view classification[J]. Chinese Journal of Engineering, 2021, 43(9): 1166-1173. DOI: 10.13374/j.issn2095-9389.2021.01.14.007
Citation: ZHANG Wen-jing, LI Wen-xiu, LIU Ai-jun, WU Xing-kun, LI Jian-feng, LUO Tao. Intelligent auxiliary diagnosis of atrial septal defect based on view classification[J]. Chinese Journal of Engineering, 2021, 43(9): 1166-1173. DOI: 10.13374/j.issn2095-9389.2021.01.14.007

基于切面识别的房间隔缺损智能辅助诊断

Intelligent auxiliary diagnosis of atrial septal defect based on view classification

  • 摘要: 针对超声心动图像质量差、噪声多,传统卷积神经网络架构对超声心动图像的学习能力有限、表达不充分的缺点,提出了一种基于标准切面识别的房间隔缺损(Atrial septal defect,ASD)智能辅助诊断模型。该模型通过对超声心动图像进行切面识别,充分融合其不同切面的语义特征,使得诊断的准确率得到明显提升。此外,还对其进行双边滤波保边去噪,并基于此模型搭建房间隔缺损智能辅助诊断系统(简称ASD辅助诊断系统)。结果表明,该ASD辅助诊断系统的准确率高达97.8%,且与传统卷积神经网络相比大大降低了假阴性率。

     

    Abstract: Atrial septal defect (ASD) is common congenital heart disease. The detection rate of congenital heart disease has increased year by year, and ASD accounted for the largest proportion of it, reaching 37.31%. The ASD patient will suffer from shortness of breath, palpitation, weakness, etc., with symptoms worsening with advanced age. The ASD patient will not suffer from congenital heart disease if their condition is diagnosed early. Echocardiography is a powerful and cost-effective means of detecting ASD. However, the disadvantages of echocardiography, such as noise and poor imaging quality, cause misdiagnosis of ASD. Hence, research into echocardiography-based efficient and effective detection of ASD with a deep neural network is of great significance. For echocardiography is noisy and fuzzy, and the learning and feature expression ability of the traditional convolutional neural network architecture is limited, a feature view classification based atrial septal defect intelligent auxiliary diagnostic model architecture was proposed. The different views of echocardiography possess different features, demanding more precise model extraction and combined features from echocardiography. The proposed model architecture integrates the semantic characteristics of several views, significantly improving the accuracy of diagnosis. In addition, with the aim of denoising and preserving edges, a bilateral filtering algorithm was performed. Furthermore, an ASD intelligent auxiliary diagnostic system was built based on the proposed model. The results show that the accuracy of the ASD auxiliary diagnostic system reaches 97.8%, and the false-negative rate is greatly reduced compared with the traditional convolutional neural network architecture.

     

/

返回文章
返回