@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",
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",
note = "15th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2019 ; Conference date: 26-09-2019 Through 28-09-2019",
}