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Automated Detection and Type Classification of Central Venous Catheters in Chest X-Rays

  • Vaishnavi Subramanian
  • Hongzhi WangEmail author
  • Joy T. Wu
  • Ken C. L. Wong
  • Arjun Sharma
  • Tanveer Syeda-Mahmood
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)

Abstract

Central venous catheters (CVCs) are commonly used in critical care settings for monitoring body functions and administering medications. They are often described in radiology reports by referring to their presence, identity and placement. In this paper, we address the problem of automatic detection of their presence and identity through automated segmentation using deep learning networks and classification based on their intersection with previously learned shape priors from clinician annotations of CVCs. The results not only outperform existing methods of catheter detection achieving 85.2% accuracy at 91.6% precision, but also enable high precision (95.2%) classification of catheter types on a large dataset of over 10,000 chest X-rays, presenting a robust and practical solution to this problem.

Keywords

Chest X-rays Catheters Classification Segmentation U-Net 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Vaishnavi Subramanian
    • 1
    • 2
  • Hongzhi Wang
    • 1
    Email author
  • Joy T. Wu
    • 1
  • Ken C. L. Wong
    • 1
  • Arjun Sharma
    • 1
  • Tanveer Syeda-Mahmood
    • 1
  1. 1.IBM Research, Almaden Research CenterSan JoseUSA
  2. 2.University of Illinois Urbana-ChampaignUrbanaUSA

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