Contour-aware multi-label chest X-ray organ segmentation

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

  • M. Kholiavchenko
  • I. Sirazitdinov
  • K. Kubrak
  • R. Badrutdinova
  • R. Kuleev
  • Y. Yuan
  • T. Vrtovec
  • Ibragimov, Bulat

Purpose: Segmentation of organs from chest X-ray images is an essential task for an accurate and reliable diagnosis of lung diseases and chest organ morphometry. In this study, we investigated the benefits of augmenting state-of-the-art deep convolutional neural networks (CNNs) for image segmentation with organ contour information and evaluated the performance of such augmentation on segmentation of lung fields, heart, and clavicles from chest X-ray images. Methods: Three state-of-the-art CNNs were augmented, namely the UNet and LinkNet architecture with the ResNeXt feature extraction backbone, and the Tiramisu architecture with the DenseNet. All CNN architectures were trained on ground-truth segmentation masks and additionally on the corresponding contours. The contribution of such contour-based augmentation was evaluated against the contour-free architectures, and 20 existing algorithms for lung field segmentation. Results: The proposed contour-aware segmentation improved the segmentation performance, and when compared against existing algorithms on the same publicly available database of 247 chest X-ray images, the UNet architecture with the ResNeXt50 encoder combined with the contour-aware approach resulted in the best overall segmentation performance, achieving a Jaccard overlap coefficient of 0.971, 0.933, and 0.903 for the lung fields, heart, and clavicles, respectively. Conclusion: In this study, we proposed to augment CNN architectures for CXR segmentation with organ contour information and were able to significantly improve segmentation accuracy and outperform all existing solution using a public chest X-ray database.

TidsskriftInternational Journal of Computer Assisted Radiology and Surgery
Udgave nummer3
Sider (fra-til)425-436
Antal sider12
StatusUdgivet - 2020

ID: 244279695