  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.
