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

Ilyas Sirazitdinov, Konstantin Kubrak, Semen Kiselev*, Alexey Tolkachev, Maksym Kholiavchenko, Bulat Ibragimov

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2020 - 29th International Conference on Artificial Neural Networks, Proceedings
EditorsIgor Farkaš, Paolo Masulli, Stefan Wermter
Number of pages11
PublisherSpringer VS
Publication date2020
Pages247-257
ISBN (Print)9783030616083
DOIs
Publication statusPublished - 2020
Event29th International Conference on Artificial Neural Networks, ICANN 2020 - Bratislava, Slovakia
Duration: 15 Sep 202018 Sep 2020

Conference

Conference29th International Conference on Artificial Neural Networks, ICANN 2020
Country/TerritorySlovakia
CityBratislava
Period15/09/202018/09/2020
SeriesLecture Notes in Computer Science
Volume12396 LNCS
ISSN0302-9743

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

  • Bone shadow exclusion
  • Bone suppression
  • Chest x-ray
  • Convolutional neural networks
  • Deep learning

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