Flow Convergence Area Estimation on In Vitro Color Flow Doppler Images Using Deep Learning

Grigorios Aris Cheimariotis; Kostas Haris; Jeesoo Lee; Brent E. White; Aggelos K. Katsaggelos; James D. Thomas; Nikolaos Maglaveras

doi:10.1007/978-3-030-31635-8_34

Flow Convergence Area Estimation on In Vitro Color Flow Doppler Images Using Deep Learning

Grigorios Aris Cheimariotis^*, Kostas Haris, Jeesoo Lee, Brent E. White, Aggelos K. Katsaggelos, James D. Thomas, Nikolaos Maglaveras

^*Corresponding author for this work

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

Abstract

We present an automatic method to estimate flow rate through the orifice in in-vitro 2D color-flow Doppler echocardiographic images. Flow rate properties are important for the assessment of pathologies like mitral regurgitation. We expect this method to be transferable to in-vivo patient data. The method consists of two main parts: (a) detecting a bounding box which encloses aliasing contours and its surroundings (namely a region representative of flow convergence area), (b) application of Convolutional Neural Networks for regression to estimate the flow convergence area. Best result achieved is the 5% mean error for validation data which is from other experiments that were used for training. Given the small number of training data, this method shows promising results.

Original language	English (US)
Title of host publication	15th Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019 - Proceedings of MEDICON 2019
Editors	Jorge Henriques, Paulo de Carvalho, Nuno Neves
Publisher	Springer
Pages	285-291
Number of pages	7
ISBN (Print)	9783030316341
DOIs	https://doi.org/10.1007/978-3-030-31635-8_34
State	Published - 2020
Event	15th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2019 - Coimbra, Portugal Duration: Sep 26 2019 → Sep 28 2019

Publication series

Name	IFMBE Proceedings
Volume	76
ISSN (Print)	1680-0737
ISSN (Electronic)	1433-9277

Conference

Conference	15th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2019
Country/Territory	Portugal
City	Coimbra
Period	9/26/19 → 9/28/19

Keywords

Color flow doppler
Deep learning
Flow rate
Mitral regurgitation

ASJC Scopus subject areas

Bioengineering
Biomedical Engineering

Access to Document

10.1007/978-3-030-31635-8_34

Cite this

Cheimariotis, G. A., Haris, K., Lee, J., White, B. E., Katsaggelos, A. K., Thomas, J. D., & Maglaveras, N. (2020). Flow Convergence Area Estimation on In Vitro Color Flow Doppler Images Using Deep Learning. In J. Henriques, P. de Carvalho, & N. Neves (Eds.), 15th Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019 - Proceedings of MEDICON 2019 (pp. 285-291). (IFMBE Proceedings; Vol. 76). Springer. https://doi.org/10.1007/978-3-030-31635-8_34

Cheimariotis, Grigorios Aris ; Haris, Kostas ; Lee, Jeesoo ; White, Brent E. ; Katsaggelos, Aggelos K. ; Thomas, James D. ; Maglaveras, Nikolaos. / Flow Convergence Area Estimation on In Vitro Color Flow Doppler Images Using Deep Learning. 15th Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019 - Proceedings of MEDICON 2019. editor / Jorge Henriques ; Paulo de Carvalho ; Nuno Neves. Springer, 2020. pp. 285-291 (IFMBE Proceedings).

@inproceedings{7b4923465d964e0894436072dfb799b0,

title = "Flow Convergence Area Estimation on In Vitro Color Flow Doppler Images Using Deep Learning",

abstract = "We present an automatic method to estimate flow rate through the orifice in in-vitro 2D color-flow Doppler echocardiographic images. Flow rate properties are important for the assessment of pathologies like mitral regurgitation. We expect this method to be transferable to in-vivo patient data. The method consists of two main parts: (a) detecting a bounding box which encloses aliasing contours and its surroundings (namely a region representative of flow convergence area), (b) application of Convolutional Neural Networks for regression to estimate the flow convergence area. Best result achieved is the 5% mean error for validation data which is from other experiments that were used for training. Given the small number of training data, this method shows promising results.",

keywords = "Color flow doppler, Deep learning, Flow rate, Mitral regurgitation",

author = "Cheimariotis, {Grigorios Aris} and Kostas Haris and Jeesoo Lee and White, {Brent E.} and Katsaggelos, {Aggelos K.} and Thomas, {James D.} and Nikolaos Maglaveras",

note = "Funding Information: Funding Sources. This research is funded by the Greek State Scholarships Foundation and European Social Fund. Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 15th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2019 ; Conference date: 26-09-2019 Through 28-09-2019",

year = "2020",

doi = "10.1007/978-3-030-31635-8_34",

language = "English (US)",

isbn = "9783030316341",

series = "IFMBE Proceedings",

publisher = "Springer",

pages = "285--291",

editor = "Jorge Henriques and {de Carvalho}, Paulo and Nuno Neves",

booktitle = "15th Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019 - Proceedings of MEDICON 2019",

}

Cheimariotis, GA, Haris, K, Lee, J, White, BE, Katsaggelos, AK , Thomas, JD & Maglaveras, N 2020, Flow Convergence Area Estimation on In Vitro Color Flow Doppler Images Using Deep Learning. in J Henriques, P de Carvalho & N Neves (eds), 15th Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019 - Proceedings of MEDICON 2019. IFMBE Proceedings, vol. 76, Springer, pp. 285-291, 15th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2019, Coimbra, Portugal, 9/26/19. https://doi.org/10.1007/978-3-030-31635-8_34

Flow Convergence Area Estimation on In Vitro Color Flow Doppler Images Using Deep Learning. / Cheimariotis, Grigorios Aris; Haris, Kostas; Lee, Jeesoo; White, Brent E.; Katsaggelos, Aggelos K.; Thomas, James D.; Maglaveras, Nikolaos.

15th Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019 - Proceedings of MEDICON 2019. ed. / Jorge Henriques; Paulo de Carvalho; Nuno Neves. Springer, 2020. p. 285-291 (IFMBE Proceedings; Vol. 76).

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

TY - GEN

T1 - Flow Convergence Area Estimation on In Vitro Color Flow Doppler Images Using Deep Learning

AU - Cheimariotis, Grigorios Aris

AU - Haris, Kostas

AU - Lee, Jeesoo

AU - White, Brent E.

AU - Katsaggelos, Aggelos K.

AU - Thomas, James D.

AU - Maglaveras, Nikolaos

PY - 2020

Y1 - 2020

N2 - We present an automatic method to estimate flow rate through the orifice in in-vitro 2D color-flow Doppler echocardiographic images. Flow rate properties are important for the assessment of pathologies like mitral regurgitation. We expect this method to be transferable to in-vivo patient data. The method consists of two main parts: (a) detecting a bounding box which encloses aliasing contours and its surroundings (namely a region representative of flow convergence area), (b) application of Convolutional Neural Networks for regression to estimate the flow convergence area. Best result achieved is the 5% mean error for validation data which is from other experiments that were used for training. Given the small number of training data, this method shows promising results.

AB - We present an automatic method to estimate flow rate through the orifice in in-vitro 2D color-flow Doppler echocardiographic images. Flow rate properties are important for the assessment of pathologies like mitral regurgitation. We expect this method to be transferable to in-vivo patient data. The method consists of two main parts: (a) detecting a bounding box which encloses aliasing contours and its surroundings (namely a region representative of flow convergence area), (b) application of Convolutional Neural Networks for regression to estimate the flow convergence area. Best result achieved is the 5% mean error for validation data which is from other experiments that were used for training. Given the small number of training data, this method shows promising results.

KW - Color flow doppler

KW - Deep learning

KW - Flow rate

KW - Mitral regurgitation

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U2 - 10.1007/978-3-030-31635-8_34

DO - 10.1007/978-3-030-31635-8_34

M3 - Conference contribution

AN - SCOPUS:85075866467

SN - 9783030316341

T3 - IFMBE Proceedings

SP - 285

EP - 291

BT - 15th Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019 - Proceedings of MEDICON 2019

A2 - Henriques, Jorge

A2 - de Carvalho, Paulo

A2 - Neves, Nuno

PB - Springer

T2 - 15th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2019

Y2 - 26 September 2019 through 28 September 2019

ER -

Cheimariotis GA, Haris K, Lee J, White BE, Katsaggelos AK , Thomas JD et al. Flow Convergence Area Estimation on In Vitro Color Flow Doppler Images Using Deep Learning. In Henriques J, de Carvalho P, Neves N, editors, 15th Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019 - Proceedings of MEDICON 2019. Springer. 2020. p. 285-291. (IFMBE Proceedings). https://doi.org/10.1007/978-3-030-31635-8_34

Flow Convergence Area Estimation on In Vitro Color Flow Doppler Images Using Deep Learning

Abstract

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Keywords

ASJC Scopus subject areas

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