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Textured Graph-Based Model of the Lungs: Application on Tuberculosis Type Classification and Multi-drug Resistance Detection

  • Yashin Dicente CidEmail author
  • Kayhan Batmanghelich
  • Henning Müller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11018)

Abstract

Tuberculosis (TB) remains a leading cause of death worldwide. Two main challenges when assessing computed tomography scans of TB patients are detecting multi-drug resistance and differentiating TB types. In this article we model the lungs as a graph entity where nodes represent anatomical lung regions and edges encode interactions between them. This graph is able to characterize the texture distribution along the lungs, making it suitable for describing patients with different TB types. In 2017, the ImageCLEF benchmark proposed a task based on computed tomography volumes of patients with TB. This task was divided into two subtasks: multi-drug resistance prediction, and TB type classification. The participation in this task showed the strength of our model, leading to best results in the competition for multi-drug resistance detection (AUC = 0.5825) and good results in the TB type classification (Cohen’s Kappa coefficient = 0.1623).

Keywords

Lung graph model 3D texture analysis Tuberculosis 

Notes

Acknowledgements

This work was partly supported by the Swiss National Science Foundation in the project PH4D (320030–146804).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yashin Dicente Cid
    • 1
    • 2
    Email author
  • Kayhan Batmanghelich
    • 3
  • Henning Müller
    • 1
    • 2
  1. 1.University of Applied Sciences Western Switzerland (HES-SO)SierreSwitzerland
  2. 2.University of GenevaGenevaSwitzerland
  3. 3.University of PittsburghPittsburghUSA

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