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 proceedingConference 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 languageEnglish (US)
Title of host publication15th Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019 - Proceedings of MEDICON 2019
EditorsJorge Henriques, Paulo de Carvalho, Nuno Neves
PublisherSpringer
Pages285-291
Number of pages7
ISBN (Print)9783030316341
DOIs
StatePublished - 2020
Event15th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2019 - Coimbra, Portugal
Duration: Sep 26 2019Sep 28 2019

Publication series

NameIFMBE Proceedings
Volume76
ISSN (Print)1680-0737
ISSN (Electronic)1433-9277

Conference

Conference15th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2019
CountryPortugal
CityCoimbra
Period9/26/199/28/19

Keywords

  • Color flow doppler
  • Deep learning
  • Flow rate
  • Mitral regurgitation

ASJC Scopus subject areas

  • Bioengineering
  • Biomedical Engineering

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  • 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