Intelligent auxiliary diagnosis of atrial septal defect based on view classification
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摘要: 針對超聲心動圖像質量差、噪聲多，傳統卷積神經網絡架構對超聲心動圖像的學習能力有限、表達不充分的缺點，提出了一種基于標準切面識別的房間隔缺損（Atrial septal defect，ASD）智能輔助診斷模型。該模型通過對超聲心動圖像進行切面識別，充分融合其不同切面的語義特征，使得診斷的準確率得到明顯提升。此外，還對其進行雙邊濾波保邊去噪，并基于此模型搭建房間隔缺損智能輔助診斷系統（簡稱ASD輔助診斷系統）。結果表明，該ASD輔助診斷系統的準確率高達97.8%，且與傳統卷積神經網絡相比大大降低了假陰性率。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.
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Key words:
- deep learning /
- echocardiography /
- atrial septal defect /
- view classification /
- bilateral filtering
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圖 1 超聲心動圖對比。（a）ASD患者；（b）健康人
Figure 1. Contrast in echocardiography of ASD patient (a) and healthy people (b)
圖 2 超聲心動圖6類標準切面。（a）胸骨旁大動脈短軸；（b）心尖四腔心；（c）胸骨旁四腔心；（d）劍突下上下腔長軸；（e）劍突下大動脈短軸；（f）劍突下雙房心
Figure 2. Six normal views of echocardiography: (a) parasternal short-axis view; (b) apical four-chamber view; (c) parasternal four-chamber view; (d) subcostal inferior vena cava; (e) subcostal short axial of aorta; (f) subcostal left and right atrium
圖 3 噪聲濾波效果對比。（a）原始圖像；（b）均值濾波；（c）高斯濾波；（d）雙邊濾波
Figure 3. Comparison of different noise filter algorithms: (a) original image; (b) mean filter; (c) Gaussian filter; (d) bilateral filter
圖 7 房間隔遮擋測試。(a，c)遮擋前；(b，d)遮擋后
Figure 7. Atrial septal covering test: (a,c) before covering; (b,d)covered
表 1 ASD診斷測試結果
Table 1. Contrast among different models
Model With bilateral
filteringAccuracy /
%False negative
rate / %False positive
rate / %Resnet50?no view-net Yes 86.7 2.8 55.6 Densenet121?no view-net Yes 86.7 13.9 11.1 Densenet?with view-net No 93.3 5.6 11 Densenet?with view-net Yes 97.8 2.8 0 
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