A novel left ventricular volumes prediction method based on deep learning network in cardiac MRI

Gongning Luo; Guanxiong Sun; Kuanquan Wang; Suyu Dong; Henggui Zhang

A novel left ventricular volumes prediction method based on deep learning network in cardiac MRI

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

^*Corresponding author for this work

Faculty of Computing

Harbin Institute of Technology
University of Manchester

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

30 Scopus citations

Abstract

Accurate estimation of left ventricle (LV) volumes plays an essential role in clinical diagnosis of cardiac diseases using MRI. Conventional methods of estimating ventricular volumes depend on the results of manual or automatic segmentation of MRI. However, manual segmentation of MRI sequences is extremely time-consuming and subjective, and automatic segmentation is still a challenging task. Therefore, this study aims to develop a new LV volumes prediction method without segmentation, motivated by deep learning technology and the large scale cardiac MRI (CMR) datasets from the second Annual Data Science Bowl (ADSB) in 2016. The experiments results shows that the predicted LV volumes have high correlation with the ground truth. These results prove that the proposed method has big potential to be researched and applied in clinical diagnosis and screening of cardiac diseases.

Original language	English
Title of host publication	Computing in Cardiology Conference, CinC 2016
Editors	Alan Murray
Publisher	IEEE Computer Society
Pages	89-92
Number of pages	4
ISBN (Electronic)	9781509008964
State	Published - 1 Mar 2016
Event	43rd Computing in Cardiology Conference, CinC 2016 - Vancouver, Canada Duration: 11 Sep 2016 → 14 Sep 2016

Publication series

Name	Computing in Cardiology
Volume	43
ISSN (Print)	2325-8861
ISSN (Electronic)	2325-887X

Conference

Conference	43rd Computing in Cardiology Conference, CinC 2016
Country/Territory	Canada
City	Vancouver
Period	11/09/16 → 14/09/16

Cite this

@inproceedings{cd36259ac77b426e83b5c8f35bb31444,

title = "A novel left ventricular volumes prediction method based on deep learning network in cardiac MRI",

abstract = "Accurate estimation of left ventricle (LV) volumes plays an essential role in clinical diagnosis of cardiac diseases using MRI. Conventional methods of estimating ventricular volumes depend on the results of manual or automatic segmentation of MRI. However, manual segmentation of MRI sequences is extremely time-consuming and subjective, and automatic segmentation is still a challenging task. Therefore, this study aims to develop a new LV volumes prediction method without segmentation, motivated by deep learning technology and the large scale cardiac MRI (CMR) datasets from the second Annual Data Science Bowl (ADSB) in 2016. The experiments results shows that the predicted LV volumes have high correlation with the ground truth. These results prove that the proposed method has big potential to be researched and applied in clinical diagnosis and screening of cardiac diseases.",

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

note = "Publisher Copyright: {\textcopyright} 2016 CCAL.; 43rd Computing in Cardiology Conference, CinC 2016 ; Conference date: 11-09-2016 Through 14-09-2016",

year = "2016",

month = mar,

day = "1",

language = "英语",

series = "Computing in Cardiology",

publisher = "IEEE Computer Society",

pages = "89--92",

editor = "Alan Murray",

booktitle = "Computing in Cardiology Conference, CinC 2016",

address = "美国",

}

Luo, G, Sun, G, Wang, K, Dong, S & Zhang, H 2016, A novel left ventricular volumes prediction method based on deep learning network in cardiac MRI. in A Murray (ed.), Computing in Cardiology Conference, CinC 2016., 7868686, Computing in Cardiology, vol. 43, IEEE Computer Society, pp. 89-92, 43rd Computing in Cardiology Conference, CinC 2016, Vancouver, Canada, 11/09/16.

A novel left ventricular volumes prediction method based on deep learning network in cardiac MRI. / Luo, Gongning; Sun, Guanxiong; Wang, Kuanquan et al.
Computing in Cardiology Conference, CinC 2016. ed. / Alan Murray. IEEE Computer Society, 2016. p. 89-92 7868686 (Computing in Cardiology; Vol. 43).

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

TY - GEN

T1 - A novel left ventricular volumes prediction method based on deep learning network in cardiac MRI

AU - Luo, Gongning

AU - Sun, Guanxiong

AU - Wang, Kuanquan

AU - Dong, Suyu

AU - Zhang, Henggui

PY - 2016/3/1

Y1 - 2016/3/1

N2 - Accurate estimation of left ventricle (LV) volumes plays an essential role in clinical diagnosis of cardiac diseases using MRI. Conventional methods of estimating ventricular volumes depend on the results of manual or automatic segmentation of MRI. However, manual segmentation of MRI sequences is extremely time-consuming and subjective, and automatic segmentation is still a challenging task. Therefore, this study aims to develop a new LV volumes prediction method without segmentation, motivated by deep learning technology and the large scale cardiac MRI (CMR) datasets from the second Annual Data Science Bowl (ADSB) in 2016. The experiments results shows that the predicted LV volumes have high correlation with the ground truth. These results prove that the proposed method has big potential to be researched and applied in clinical diagnosis and screening of cardiac diseases.

AB - Accurate estimation of left ventricle (LV) volumes plays an essential role in clinical diagnosis of cardiac diseases using MRI. Conventional methods of estimating ventricular volumes depend on the results of manual or automatic segmentation of MRI. However, manual segmentation of MRI sequences is extremely time-consuming and subjective, and automatic segmentation is still a challenging task. Therefore, this study aims to develop a new LV volumes prediction method without segmentation, motivated by deep learning technology and the large scale cardiac MRI (CMR) datasets from the second Annual Data Science Bowl (ADSB) in 2016. The experiments results shows that the predicted LV volumes have high correlation with the ground truth. These results prove that the proposed method has big potential to be researched and applied in clinical diagnosis and screening of cardiac diseases.

UR - https://www.scopus.com/pages/publications/85016133285

M3 - 会议稿件

AN - SCOPUS:85016133285

T3 - Computing in Cardiology

SP - 89

EP - 92

BT - Computing in Cardiology Conference, CinC 2016

A2 - Murray, Alan

PB - IEEE Computer Society

T2 - 43rd Computing in Cardiology Conference, CinC 2016

Y2 - 11 September 2016 through 14 September 2016

ER -

A novel left ventricular volumes prediction method based on deep learning network in cardiac MRI

Abstract

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