Left Ventricle Quantification with Cardiac MRI: Deep Learning Meets Statistical Models of Deformation

Jorge Corral Acero; Hao Xu; Ernesto Zacur; Jurgen E. Schneider; Pablo Lamata; Alfonso Bueno-Orovio; Vicente Grau

doi:10.1007/978-3-030-39074-7_40

Left Ventricle Quantification with Cardiac MRI: Deep Learning Meets Statistical Models of Deformation

Jorge Corral Acero^*, Hao Xu, Ernesto Zacur, Jurgen E. Schneider, Pablo Lamata, Alfonso Bueno-Orovio, Vicente Grau

^*Corresponding author for this work

Biomedical Engineering Department

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

4 Citations (Scopus)

Abstract

Deep learning has been widely applied for left ventricle (LV) analysis, obtaining state of the art results in quantification through image segmentation. When the training datasets are limited, data augmentation becomes critical, but standard augmentation methods do not usually incorporate the natural variation of anatomy. In this paper we propose a pipeline for LV quantification applying our data augmentation methodology based on statistical models of deformations (SMOD) to quantify LV based on segmentation of cardiac MR (CMR) images, and present an in-depth analysis of the effects of deformation parameters in SMOD performance. We trained and evaluated our pipeline on the MICCAI 2019 Left Ventricle Full Quantification Challenge dataset, and achieved average mean absolute error (MAE) for areas, dimensions, regional wall thickness and phase of 106 mm², 1.52 mm, 1.01 mm and 8.0% respectively in a 3-fold cross-validation experiment.

Original language	English
Title of host publication	Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers
Editors	Mihaela Pop, Maxime Sermesant, Oscar Camara, Xiahai Zhuang, Shuo Li, Alistair Young, Tommaso Mansi, Avan Suinesiaputra
Publisher	SPRINGER
Pages	384-394
Number of pages	11
ISBN (Print)	9783030390730
DOIs	https://doi.org/10.1007/978-3-030-39074-7_40
Publication status	Published - 1 Jan 2020
Event	10th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: 13 Oct 2019 → 13 Oct 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12009 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
Country/Territory	China
City	Shenzhen
Period	13/10/2019 → 13/10/2019

Keywords

Data augmentation
Deep learning
LV quantification

Access to Document

10.1007/978-3-030-39074-7_40

Cite this

Corral Acero, J., Xu, H., Zacur, E., Schneider, J. E., Lamata, P., Bueno-Orovio, A., & Grau, V. (2020). Left Ventricle Quantification with Cardiac MRI: Deep Learning Meets Statistical Models of Deformation. In M. Pop, M. Sermesant, O. Camara, X. Zhuang, S. Li, A. Young, T. Mansi, & A. Suinesiaputra (Eds.), Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers (pp. 384-394). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12009 LNCS). SPRINGER. https://doi.org/10.1007/978-3-030-39074-7_40

Corral Acero, Jorge ; Xu, Hao ; Zacur, Ernesto et al. / Left Ventricle Quantification with Cardiac MRI : Deep Learning Meets Statistical Models of Deformation. Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers. editor / Mihaela Pop ; Maxime Sermesant ; Oscar Camara ; Xiahai Zhuang ; Shuo Li ; Alistair Young ; Tommaso Mansi ; Avan Suinesiaputra. SPRINGER, 2020. pp. 384-394 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inbook{0b7332f58dd94ccc889d31d99a0686e5,

title = "Left Ventricle Quantification with Cardiac MRI: Deep Learning Meets Statistical Models of Deformation",

abstract = "Deep learning has been widely applied for left ventricle (LV) analysis, obtaining state of the art results in quantification through image segmentation. When the training datasets are limited, data augmentation becomes critical, but standard augmentation methods do not usually incorporate the natural variation of anatomy. In this paper we propose a pipeline for LV quantification applying our data augmentation methodology based on statistical models of deformations (SMOD) to quantify LV based on segmentation of cardiac MR (CMR) images, and present an in-depth analysis of the effects of deformation parameters in SMOD performance. We trained and evaluated our pipeline on the MICCAI 2019 Left Ventricle Full Quantification Challenge dataset, and achieved average mean absolute error (MAE) for areas, dimensions, regional wall thickness and phase of 106 mm2, 1.52 mm, 1.01 mm and 8.0% respectively in a 3-fold cross-validation experiment.",

keywords = "Data augmentation, Deep learning, LV quantification",

author = "{Corral Acero}, Jorge and Hao Xu and Ernesto Zacur and Schneider, {Jurgen E.} and Pablo Lamata and Alfonso Bueno-Orovio and Vicente Grau",

year = "2020",

month = jan,

day = "1",

doi = "10.1007/978-3-030-39074-7_40",

language = "English",

isbn = "9783030390730",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "SPRINGER",

pages = "384--394",

editor = "Mihaela Pop and Maxime Sermesant and Oscar Camara and Xiahai Zhuang and Shuo Li and Alistair Young and Tommaso Mansi and Avan Suinesiaputra",

booktitle = "Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers",

note = "10th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019 ; Conference date: 13-10-2019 Through 13-10-2019",

}

Corral Acero, J, Xu, H, Zacur, E, Schneider, JE, Lamata, P, Bueno-Orovio, A & Grau, V 2020, Left Ventricle Quantification with Cardiac MRI: Deep Learning Meets Statistical Models of Deformation. in M Pop, M Sermesant, O Camara, X Zhuang, S Li, A Young, T Mansi & A Suinesiaputra (eds), Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12009 LNCS, SPRINGER, pp. 384-394, 10th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019, Shenzhen, China, 13/10/2019. https://doi.org/10.1007/978-3-030-39074-7_40

Left Ventricle Quantification with Cardiac MRI : Deep Learning Meets Statistical Models of Deformation. / Corral Acero, Jorge; Xu, Hao; Zacur, Ernesto et al.

Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers. ed. / Mihaela Pop; Maxime Sermesant; Oscar Camara; Xiahai Zhuang; Shuo Li; Alistair Young; Tommaso Mansi; Avan Suinesiaputra. SPRINGER, 2020. p. 384-394 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12009 LNCS).

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

TY - CHAP

T1 - Left Ventricle Quantification with Cardiac MRI

T2 - 10th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019

AU - Corral Acero, Jorge

AU - Xu, Hao

AU - Zacur, Ernesto

AU - Schneider, Jurgen E.

AU - Lamata, Pablo

AU - Bueno-Orovio, Alfonso

AU - Grau, Vicente

PY - 2020/1/1

Y1 - 2020/1/1

N2 - Deep learning has been widely applied for left ventricle (LV) analysis, obtaining state of the art results in quantification through image segmentation. When the training datasets are limited, data augmentation becomes critical, but standard augmentation methods do not usually incorporate the natural variation of anatomy. In this paper we propose a pipeline for LV quantification applying our data augmentation methodology based on statistical models of deformations (SMOD) to quantify LV based on segmentation of cardiac MR (CMR) images, and present an in-depth analysis of the effects of deformation parameters in SMOD performance. We trained and evaluated our pipeline on the MICCAI 2019 Left Ventricle Full Quantification Challenge dataset, and achieved average mean absolute error (MAE) for areas, dimensions, regional wall thickness and phase of 106 mm2, 1.52 mm, 1.01 mm and 8.0% respectively in a 3-fold cross-validation experiment.

AB - Deep learning has been widely applied for left ventricle (LV) analysis, obtaining state of the art results in quantification through image segmentation. When the training datasets are limited, data augmentation becomes critical, but standard augmentation methods do not usually incorporate the natural variation of anatomy. In this paper we propose a pipeline for LV quantification applying our data augmentation methodology based on statistical models of deformations (SMOD) to quantify LV based on segmentation of cardiac MR (CMR) images, and present an in-depth analysis of the effects of deformation parameters in SMOD performance. We trained and evaluated our pipeline on the MICCAI 2019 Left Ventricle Full Quantification Challenge dataset, and achieved average mean absolute error (MAE) for areas, dimensions, regional wall thickness and phase of 106 mm2, 1.52 mm, 1.01 mm and 8.0% respectively in a 3-fold cross-validation experiment.

KW - Data augmentation

KW - Deep learning

KW - LV quantification

UR - http://www.scopus.com/inward/record.url?scp=85081930358&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-39074-7_40

DO - 10.1007/978-3-030-39074-7_40

M3 - Conference paper

AN - SCOPUS:85081930358

SN - 9783030390730

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 384

EP - 394

BT - Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers

A2 - Pop, Mihaela

A2 - Sermesant, Maxime

A2 - Camara, Oscar

A2 - Zhuang, Xiahai

A2 - Li, Shuo

A2 - Young, Alistair

A2 - Mansi, Tommaso

A2 - Suinesiaputra, Avan

PB - SPRINGER

Y2 - 13 October 2019 through 13 October 2019

ER -

Corral Acero J, Xu H, Zacur E, Schneider JE, Lamata P, Bueno-Orovio A et al. Left Ventricle Quantification with Cardiac MRI: Deep Learning Meets Statistical Models of Deformation. In Pop M, Sermesant M, Camara O, Zhuang X, Li S, Young A, Mansi T, Suinesiaputra A, editors, Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers. SPRINGER. 2020. p. 384-394. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-39074-7_40

Left Ventricle Quantification with Cardiac MRI: Deep Learning Meets Statistical Models of Deformation

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this