Evaluation of Deep Learning Methods for Bone Suppression from Dual Energy Chest Radiography

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Standard

Evaluation of Deep Learning Methods for Bone Suppression from Dual Energy Chest Radiography. / Sirazitdinov, Ilyas; Kubrak, Konstantin; Kiselev, Semen; Tolkachev, Alexey; Kholiavchenko, Maksym; Ibragimov, Bulat.

Artificial Neural Networks and Machine Learning – ICANN 2020 - 29th International Conference on Artificial Neural Networks, Proceedings. red. / Igor Farkaš; Paolo Masulli; Stefan Wermter. Springer VS, 2020. s. 247-257 (Lecture Notes in Computer Science, Bind 12396 LNCS).

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Harvard

Sirazitdinov, I, Kubrak, K, Kiselev, S, Tolkachev, A, Kholiavchenko, M & Ibragimov, B 2020, Evaluation of Deep Learning Methods for Bone Suppression from Dual Energy Chest Radiography. i I Farkaš, P Masulli & S Wermter (red), Artificial Neural Networks and Machine Learning – ICANN 2020 - 29th International Conference on Artificial Neural Networks, Proceedings. Springer VS, Lecture Notes in Computer Science, bind 12396 LNCS, s. 247-257, 29th International Conference on Artificial Neural Networks, ICANN 2020, Bratislava, Slovakiet, 15/09/2020. https://doi.org/10.1007/978-3-030-61609-0_20

APA

Sirazitdinov, I., Kubrak, K., Kiselev, S., Tolkachev, A., Kholiavchenko, M., & Ibragimov, B. (2020). Evaluation of Deep Learning Methods for Bone Suppression from Dual Energy Chest Radiography. I I. Farkaš, P. Masulli, & S. Wermter (red.), Artificial Neural Networks and Machine Learning – ICANN 2020 - 29th International Conference on Artificial Neural Networks, Proceedings (s. 247-257). Springer VS. Lecture Notes in Computer Science Bind 12396 LNCS https://doi.org/10.1007/978-3-030-61609-0_20

Vancouver

Sirazitdinov I, Kubrak K, Kiselev S, Tolkachev A, Kholiavchenko M, Ibragimov B. Evaluation of Deep Learning Methods for Bone Suppression from Dual Energy Chest Radiography. I Farkaš I, Masulli P, Wermter S, red., Artificial Neural Networks and Machine Learning – ICANN 2020 - 29th International Conference on Artificial Neural Networks, Proceedings. Springer VS. 2020. s. 247-257. (Lecture Notes in Computer Science, Bind 12396 LNCS). https://doi.org/10.1007/978-3-030-61609-0_20

Author

Sirazitdinov, Ilyas ; Kubrak, Konstantin ; Kiselev, Semen ; Tolkachev, Alexey ; Kholiavchenko, Maksym ; Ibragimov, Bulat. / Evaluation of Deep Learning Methods for Bone Suppression from Dual Energy Chest Radiography. Artificial Neural Networks and Machine Learning – ICANN 2020 - 29th International Conference on Artificial Neural Networks, Proceedings. red. / Igor Farkaš ; Paolo Masulli ; Stefan Wermter. Springer VS, 2020. s. 247-257 (Lecture Notes in Computer Science, Bind 12396 LNCS).

Bibtex

@inproceedings{9cfd05fb55d2428d8c817bff41bcb6d1,
title = "Evaluation of Deep Learning Methods for Bone Suppression from Dual Energy Chest Radiography",
abstract = "Bone suppression in chest x-rays is an important processing step that can often improve visual detection of lung pathologies hidden under ribs or clavicle shadows. Current diagnostic imaging protocol does not include hardware-based bone suppression, hence the need for a software-based solution. This paper evaluates various deep learning models adapted for bone suppression task, namely, we implemented several state-of-the-art deep learning architectures: convolution autoencoder, U-net, FPN, cGAN; augmented them with domain-specific denoising techniques, such as wavelet decomposition, with the aim to identify the optimal solution for chest x-ray analysis. Our results show that wavelet decomposition does not improve the rib suppression, “skip connections” modification outperforms baseline autoencoder approach with and without the usage of the wavelet decomposition, the residual models are trained faster than plain models and achieve higher validation scores.",
keywords = "Bone shadow exclusion, Bone suppression, Chest x-ray, Convolutional neural networks, Deep learning",
author = "Ilyas Sirazitdinov and Konstantin Kubrak and Semen Kiselev and Alexey Tolkachev and Maksym Kholiavchenko and Bulat Ibragimov",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 29th International Conference on Artificial Neural Networks, ICANN 2020 ; Conference date: 15-09-2020 Through 18-09-2020",
year = "2020",
doi = "10.1007/978-3-030-61609-0_20",
language = "English",
isbn = "9783030616083",
series = "Lecture Notes in Computer Science",
publisher = "Springer VS",
pages = "247--257",
editor = "Igor Farka{\v s} and Paolo Masulli and Stefan Wermter",
booktitle = "Artificial Neural Networks and Machine Learning – ICANN 2020 - 29th International Conference on Artificial Neural Networks, Proceedings",

}

RIS

TY - GEN

T1 - Evaluation of Deep Learning Methods for Bone Suppression from Dual Energy Chest Radiography

AU - Sirazitdinov, Ilyas

AU - Kubrak, Konstantin

AU - Kiselev, Semen

AU - Tolkachev, Alexey

AU - Kholiavchenko, Maksym

AU - Ibragimov, Bulat

N1 - Publisher Copyright: © 2020, Springer Nature Switzerland AG.

PY - 2020

Y1 - 2020

N2 - Bone suppression in chest x-rays is an important processing step that can often improve visual detection of lung pathologies hidden under ribs or clavicle shadows. Current diagnostic imaging protocol does not include hardware-based bone suppression, hence the need for a software-based solution. This paper evaluates various deep learning models adapted for bone suppression task, namely, we implemented several state-of-the-art deep learning architectures: convolution autoencoder, U-net, FPN, cGAN; augmented them with domain-specific denoising techniques, such as wavelet decomposition, with the aim to identify the optimal solution for chest x-ray analysis. Our results show that wavelet decomposition does not improve the rib suppression, “skip connections” modification outperforms baseline autoencoder approach with and without the usage of the wavelet decomposition, the residual models are trained faster than plain models and achieve higher validation scores.

AB - Bone suppression in chest x-rays is an important processing step that can often improve visual detection of lung pathologies hidden under ribs or clavicle shadows. Current diagnostic imaging protocol does not include hardware-based bone suppression, hence the need for a software-based solution. This paper evaluates various deep learning models adapted for bone suppression task, namely, we implemented several state-of-the-art deep learning architectures: convolution autoencoder, U-net, FPN, cGAN; augmented them with domain-specific denoising techniques, such as wavelet decomposition, with the aim to identify the optimal solution for chest x-ray analysis. Our results show that wavelet decomposition does not improve the rib suppression, “skip connections” modification outperforms baseline autoencoder approach with and without the usage of the wavelet decomposition, the residual models are trained faster than plain models and achieve higher validation scores.

KW - Bone shadow exclusion

KW - Bone suppression

KW - Chest x-ray

KW - Convolutional neural networks

KW - Deep learning

UR - http://www.scopus.com/inward/record.url?scp=85096520213&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-61609-0_20

DO - 10.1007/978-3-030-61609-0_20

M3 - Article in proceedings

AN - SCOPUS:85096520213

SN - 9783030616083

T3 - Lecture Notes in Computer Science

SP - 247

EP - 257

BT - Artificial Neural Networks and Machine Learning – ICANN 2020 - 29th International Conference on Artificial Neural Networks, Proceedings

A2 - Farkaš, Igor

A2 - Masulli, Paolo

A2 - Wermter, Stefan

PB - Springer VS

T2 - 29th International Conference on Artificial Neural Networks, ICANN 2020

Y2 - 15 September 2020 through 18 September 2020

ER -

ID: 271604995