Deep RetinaNet for Dynamic Left Ventricle Detection in Multiview Echocardiography Classification
Joint Authors
Cui, Lizhen
Xiao, Xiaoyan
Yang, Meijun
Liu, Zhi
Sun, Longkun
Sun, Dianmin
Zhang, Pengfei
Guo, Wei
Yang, Guang
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-01
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
Background.
Currently, echocardiography has become an essential technology for the diagnosis of cardiovascular diseases.
Accurate classification of apical two-chamber (A2C), apical three-chamber (A3C), and apical four-chamber (A4C) views and the precise detection of the left ventricle can significantly reduce the workload of clinicians and improve the reproducibility of left ventricle segmentation.
In addition, left ventricle detection is significant for the three-dimensional reconstruction of the heart chambers.
Method.
RetinaNet is a one-stage object detection algorithm that can achieve high accuracy and efficiency at the same time.
RetinaNet is mainly composed of the residual network (ResNet), the feature pyramid network (FPN), and two fully convolutional networks (FCNs); one FCN is for the classification task, and the other is for the border regression task.
Results.
In this paper, we use the classification subnetwork to classify A2C, A3C, and A4C images and use the regression subnetworks to detect the left ventricle simultaneously.
We display not only the position of the left ventricle on the test image but also the view category on the image, which will facilitate the diagnosis.
We used the mean intersection-over-union (mIOU) as an index to measure the performance of left ventricle detection and the accuracy as an index to measure the effect of the classification of the three different views.
Our study shows that both classification and detection effects are noteworthy.
The classification accuracy rates of A2C, A3C, and A4C are 1.000, 0.935, and 0.989, respectively.
The mIOU values of A2C, A3C, and A4C are 0.858, 0.794, and 0.838, respectively.
American Psychological Association (APA)
Yang, Meijun& Xiao, Xiaoyan& Liu, Zhi& Sun, Longkun& Guo, Wei& Cui, Lizhen…[et al.]. 2020. Deep RetinaNet for Dynamic Left Ventricle Detection in Multiview Echocardiography Classification. Scientific Programming،Vol. 2020, no. 2020, pp.1-6.
https://search.emarefa.net/detail/BIM-1209098
Modern Language Association (MLA)
Yang, Meijun…[et al.]. Deep RetinaNet for Dynamic Left Ventricle Detection in Multiview Echocardiography Classification. Scientific Programming No. 2020 (2020), pp.1-6.
https://search.emarefa.net/detail/BIM-1209098
American Medical Association (AMA)
Yang, Meijun& Xiao, Xiaoyan& Liu, Zhi& Sun, Longkun& Guo, Wei& Cui, Lizhen…[et al.]. Deep RetinaNet for Dynamic Left Ventricle Detection in Multiview Echocardiography Classification. Scientific Programming. 2020. Vol. 2020, no. 2020, pp.1-6.
https://search.emarefa.net/detail/BIM-1209098
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1209098