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

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

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

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.

TitelArtificial Neural Networks and Machine Learning – ICANN 2020 - 29th International Conference on Artificial Neural Networks, Proceedings
RedaktørerIgor Farkaš, Paolo Masulli, Stefan Wermter
Antal sider11
ForlagSpringer VS
ISBN (Trykt)9783030616083
StatusUdgivet - 2020
Begivenhed29th International Conference on Artificial Neural Networks, ICANN 2020 - Bratislava, Slovakiet
Varighed: 15 sep. 202018 sep. 2020


Konference29th International Conference on Artificial Neural Networks, ICANN 2020
NavnLecture Notes in Computer Science
Vol/bind12396 LNCS

ID: 271604995