Cardiac left ventricular volumes prediction method based on atlas location and deep learning

Gongning Luo; Suyu Dong; Kuanquan Wang; Henggui Zhang

doi:10.1109/BIBM.2016.7822759

Cardiac left ventricular volumes prediction method based on atlas location and deep learning

Gongning Luo
, Suyu Dong
, Kuanquan Wang^*
, Henggui Zhang

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

15 Scopus citations

Abstract

In this paper, we proposed a novel left ventricular volumes prediction method. This method is a cascade architecture which is based on multi-scale LV atlas location and deep convolutional neural networks (CNN). Firstly, we adopted LV atlas mapping method to achieve accurate location of LV region in cardiac magnetic resonance (CMR) images. And then, the CNN were used to train an end-to-end LV volumes prediction model to achieve the direct prediction. What's more, the large number of CMR images data (1140 subjects, more than 1026000 images) make the proposed deep CNN have relatively better feature representation and robust prediction ability. The experiment results on the large-scale CMR datasets prove that the proposed method has higher accuracy than the state-of-the-art prediction methods in terms of the end-diastole volumes (EDV), the end-systole volumes (ESV), and the ejection fraction (EF). Besides, we make the proposed method open accessible to public for wide application in other biomedical image processing fields.

Original language	English
Title of host publication	Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Editors	Kevin Burrage, Qian Zhu, Yunlong Liu, Tianhai Tian, Yadong Wang, Xiaohua Tony Hu, Qinghua Jiang, Jiangning Song, Shinichi Morishita, Kevin Burrage, Guohua Wang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1604-1610
Number of pages	7
ISBN (Electronic)	9781509016105
DOIs	https://doi.org/10.1109/BIBM.2016.7822759
State	Published - 17 Jan 2017
Externally published	Yes
Event	2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 - Shenzhen, China Duration: 15 Dec 2016 → 18 Dec 2016

Publication series

Name	Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

Conference

Conference	2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Country/Territory	China
City	Shenzhen
Period	15/12/16 → 18/12/16

Keywords

CMR images
Deep convolutional neural networks
LV atlas
Volumes prediction

Access to Document

10.1109/BIBM.2016.7822759

Cite this

Luo, G., Dong, S., Wang, K., & Zhang, H. (2017). Cardiac left ventricular volumes prediction method based on atlas location and deep learning. In K. Burrage, Q. Zhu, Y. Liu, T. Tian, Y. Wang, X. T. Hu, Q. Jiang, J. Song, S. Morishita, K. Burrage, & G. Wang (Eds.), Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 (pp. 1604-1610). Article 7822759 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2016.7822759

Luo, Gongning ; Dong, Suyu ; Wang, Kuanquan et al. / Cardiac left ventricular volumes prediction method based on atlas location and deep learning. Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. editor / Kevin Burrage ; Qian Zhu ; Yunlong Liu ; Tianhai Tian ; Yadong Wang ; Xiaohua Tony Hu ; Qinghua Jiang ; Jiangning Song ; Shinichi Morishita ; Kevin Burrage ; Guohua Wang. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1604-1610 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016).

@inproceedings{40002227129943c4bdf69f90bcfee3eb,

title = "Cardiac left ventricular volumes prediction method based on atlas location and deep learning",

abstract = "In this paper, we proposed a novel left ventricular volumes prediction method. This method is a cascade architecture which is based on multi-scale LV atlas location and deep convolutional neural networks (CNN). Firstly, we adopted LV atlas mapping method to achieve accurate location of LV region in cardiac magnetic resonance (CMR) images. And then, the CNN were used to train an end-to-end LV volumes prediction model to achieve the direct prediction. What's more, the large number of CMR images data (1140 subjects, more than 1026000 images) make the proposed deep CNN have relatively better feature representation and robust prediction ability. The experiment results on the large-scale CMR datasets prove that the proposed method has higher accuracy than the state-of-the-art prediction methods in terms of the end-diastole volumes (EDV), the end-systole volumes (ESV), and the ejection fraction (EF). Besides, we make the proposed method open accessible to public for wide application in other biomedical image processing fields.",

keywords = "CMR images, Deep convolutional neural networks, LV atlas, Volumes prediction",

author = "Gongning Luo and Suyu Dong and Kuanquan Wang and Henggui Zhang",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 ; Conference date: 15-12-2016 Through 18-12-2016",

year = "2017",

month = jan,

day = "17",

doi = "10.1109/BIBM.2016.7822759",

language = "英语",

series = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1604--1610",

editor = "Kevin Burrage and Qian Zhu and Yunlong Liu and Tianhai Tian and Yadong Wang and Hu, \{Xiaohua Tony\} and Qinghua Jiang and Jiangning Song and Shinichi Morishita and Kevin Burrage and Guohua Wang",

booktitle = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",

address = "美国",

}

Luo, G, Dong, S, Wang, K & Zhang, H 2017, Cardiac left ventricular volumes prediction method based on atlas location and deep learning. in K Burrage, Q Zhu, Y Liu, T Tian, Y Wang, XT Hu, Q Jiang, J Song, S Morishita, K Burrage & G Wang (eds), Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016., 7822759, Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, Institute of Electrical and Electronics Engineers Inc., pp. 1604-1610, 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, Shenzhen, China, 15/12/16. https://doi.org/10.1109/BIBM.2016.7822759

Cardiac left ventricular volumes prediction method based on atlas location and deep learning. / Luo, Gongning; Dong, Suyu; Wang, Kuanquan et al.
Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. ed. / Kevin Burrage; Qian Zhu; Yunlong Liu; Tianhai Tian; Yadong Wang; Xiaohua Tony Hu; Qinghua Jiang; Jiangning Song; Shinichi Morishita; Kevin Burrage; Guohua Wang. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1604-1610 7822759 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Cardiac left ventricular volumes prediction method based on atlas location and deep learning

AU - Luo, Gongning

AU - Dong, Suyu

AU - Wang, Kuanquan

AU - Zhang, Henggui

PY - 2017/1/17

Y1 - 2017/1/17

N2 - In this paper, we proposed a novel left ventricular volumes prediction method. This method is a cascade architecture which is based on multi-scale LV atlas location and deep convolutional neural networks (CNN). Firstly, we adopted LV atlas mapping method to achieve accurate location of LV region in cardiac magnetic resonance (CMR) images. And then, the CNN were used to train an end-to-end LV volumes prediction model to achieve the direct prediction. What's more, the large number of CMR images data (1140 subjects, more than 1026000 images) make the proposed deep CNN have relatively better feature representation and robust prediction ability. The experiment results on the large-scale CMR datasets prove that the proposed method has higher accuracy than the state-of-the-art prediction methods in terms of the end-diastole volumes (EDV), the end-systole volumes (ESV), and the ejection fraction (EF). Besides, we make the proposed method open accessible to public for wide application in other biomedical image processing fields.

AB - In this paper, we proposed a novel left ventricular volumes prediction method. This method is a cascade architecture which is based on multi-scale LV atlas location and deep convolutional neural networks (CNN). Firstly, we adopted LV atlas mapping method to achieve accurate location of LV region in cardiac magnetic resonance (CMR) images. And then, the CNN were used to train an end-to-end LV volumes prediction model to achieve the direct prediction. What's more, the large number of CMR images data (1140 subjects, more than 1026000 images) make the proposed deep CNN have relatively better feature representation and robust prediction ability. The experiment results on the large-scale CMR datasets prove that the proposed method has higher accuracy than the state-of-the-art prediction methods in terms of the end-diastole volumes (EDV), the end-systole volumes (ESV), and the ejection fraction (EF). Besides, we make the proposed method open accessible to public for wide application in other biomedical image processing fields.

KW - CMR images

KW - Deep convolutional neural networks

KW - LV atlas

KW - Volumes prediction

UR - https://www.scopus.com/pages/publications/85013296176

U2 - 10.1109/BIBM.2016.7822759

DO - 10.1109/BIBM.2016.7822759

M3 - 会议稿件

AN - SCOPUS:85013296176

T3 - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

SP - 1604

EP - 1610

BT - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

A2 - Burrage, Kevin

A2 - Zhu, Qian

A2 - Liu, Yunlong

A2 - Tian, Tianhai

A2 - Wang, Yadong

A2 - Hu, Xiaohua Tony

A2 - Jiang, Qinghua

A2 - Song, Jiangning

A2 - Morishita, Shinichi

A2 - Burrage, Kevin

A2 - Wang, Guohua

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

Y2 - 15 December 2016 through 18 December 2016

ER -

Luo G, Dong S, Wang K, Zhang H. Cardiac left ventricular volumes prediction method based on atlas location and deep learning. In Burrage K, Zhu Q, Liu Y, Tian T, Wang Y, Hu XT, Jiang Q, Song J, Morishita S, Burrage K, Wang G, editors, Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1604-1610. 7822759. (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016). doi: 10.1109/BIBM.2016.7822759

Cardiac left ventricular volumes prediction method based on atlas location and deep learning

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