Automating cardiothoracic ratio measurements in chest X-rays

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Standard

Automating cardiothoracic ratio measurements in chest X-rays. / Kiselev, Semen; Maksudov, Bulat; Mustafaev, Tamerlan; Kuleev, Ramil; Ibragimov, Bulat.

2021 International Conference "Nonlinearity, Information and Robotics", NIR 2021. IEEE, 2021.

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Harvard

Kiselev, S, Maksudov, B, Mustafaev, T, Kuleev, R & Ibragimov, B 2021, Automating cardiothoracic ratio measurements in chest X-rays. i 2021 International Conference "Nonlinearity, Information and Robotics", NIR 2021. IEEE, 2021 International Conference "Nonlinearity, Information and Robotics", NIR 2021, Innopolis, Rusland, 26/08/2021. https://doi.org/10.1109/NIR52917.2021.9666142

APA

Kiselev, S., Maksudov, B., Mustafaev, T., Kuleev, R., & Ibragimov, B. (2021). Automating cardiothoracic ratio measurements in chest X-rays. I 2021 International Conference "Nonlinearity, Information and Robotics", NIR 2021 IEEE. https://doi.org/10.1109/NIR52917.2021.9666142

Vancouver

Kiselev S, Maksudov B, Mustafaev T, Kuleev R, Ibragimov B. Automating cardiothoracic ratio measurements in chest X-rays. I 2021 International Conference "Nonlinearity, Information and Robotics", NIR 2021. IEEE. 2021 https://doi.org/10.1109/NIR52917.2021.9666142

Author

Kiselev, Semen ; Maksudov, Bulat ; Mustafaev, Tamerlan ; Kuleev, Ramil ; Ibragimov, Bulat. / Automating cardiothoracic ratio measurements in chest X-rays. 2021 International Conference "Nonlinearity, Information and Robotics", NIR 2021. IEEE, 2021.

Bibtex

@inproceedings{41c70ac68d244c6c928e96959cf12438,
title = "Automating cardiothoracic ratio measurements in chest X-rays",
abstract = "The analysis of the positions, shapes, and sizes of thoracic organs is an internationally established practice for radiologists. The considerable amount of time spent on manual measurements of roentgenographic features reveals the need for a computerized approach for the automation of these measurements. In this work, we introduce a new way for the annotation of the chest x-ray data and evaluation of the most commonly-calculated morphometric parameter - cardiothoracic ratio. The measurement of interest was defined as ratio of line segments outlining the heart and the distance between two most lateral landmarks on lung fields. Using a manually annotated dataset, we developed a hourglass-based deep learning-based model to detect these landmarks and perform the measurement. We found that the predictions of the proposed solution differ from the annotation of an expert radiologist in 9.8mm error measured in terms of the mean Euclidean distance. ",
author = "Semen Kiselev and Bulat Maksudov and Tamerlan Mustafaev and Ramil Kuleev and Bulat Ibragimov",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Conference {"}Nonlinearity, Information and Robotics{"}, NIR 2021 ; Conference date: 26-08-2021 Through 29-08-2021",
year = "2021",
doi = "10.1109/NIR52917.2021.9666142",
language = "English",
booktitle = "2021 International Conference {"}Nonlinearity, Information and Robotics{"}, NIR 2021",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Automating cardiothoracic ratio measurements in chest X-rays

AU - Kiselev, Semen

AU - Maksudov, Bulat

AU - Mustafaev, Tamerlan

AU - Kuleev, Ramil

AU - Ibragimov, Bulat

N1 - Publisher Copyright: © 2021 IEEE.

PY - 2021

Y1 - 2021

N2 - The analysis of the positions, shapes, and sizes of thoracic organs is an internationally established practice for radiologists. The considerable amount of time spent on manual measurements of roentgenographic features reveals the need for a computerized approach for the automation of these measurements. In this work, we introduce a new way for the annotation of the chest x-ray data and evaluation of the most commonly-calculated morphometric parameter - cardiothoracic ratio. The measurement of interest was defined as ratio of line segments outlining the heart and the distance between two most lateral landmarks on lung fields. Using a manually annotated dataset, we developed a hourglass-based deep learning-based model to detect these landmarks and perform the measurement. We found that the predictions of the proposed solution differ from the annotation of an expert radiologist in 9.8mm error measured in terms of the mean Euclidean distance.

AB - The analysis of the positions, shapes, and sizes of thoracic organs is an internationally established practice for radiologists. The considerable amount of time spent on manual measurements of roentgenographic features reveals the need for a computerized approach for the automation of these measurements. In this work, we introduce a new way for the annotation of the chest x-ray data and evaluation of the most commonly-calculated morphometric parameter - cardiothoracic ratio. The measurement of interest was defined as ratio of line segments outlining the heart and the distance between two most lateral landmarks on lung fields. Using a manually annotated dataset, we developed a hourglass-based deep learning-based model to detect these landmarks and perform the measurement. We found that the predictions of the proposed solution differ from the annotation of an expert radiologist in 9.8mm error measured in terms of the mean Euclidean distance.

U2 - 10.1109/NIR52917.2021.9666142

DO - 10.1109/NIR52917.2021.9666142

M3 - Article in proceedings

AN - SCOPUS:85124700087

BT - 2021 International Conference "Nonlinearity, Information and Robotics", NIR 2021

PB - IEEE

T2 - 2021 International Conference "Nonlinearity, Information and Robotics", NIR 2021

Y2 - 26 August 2021 through 29 August 2021

ER -

ID: 306987473