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Proceedings Paper

Catheter detection and classification on chest radiographs: an automated prototype computer-aided detection (CAD) system for radiologists
Author(s): Bharath Ramakrishna; Matthew Brown; Jonathan Goldin; Chris Cagnon; Dieter Enzmann
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Paper Abstract

Chest radiographs are the quickest and safest method to check placement of man-made medical devices placed in the body like catheters, stents and pacemakers etc out of which catheters are the most commonly used devices. The two most often used catheters especially in the ICU are the Endotracheal (ET) tube used to maintain patient's airway and the Nasogastric (NG) tube used to feed and administer drugs. Tertiary ICU's typically generate over 250 chest radiographs per day to confirm tube placement. Incorrect tube placements can cause serious complications and can even be fatal. The task of identifying these tubes on chest radiographs is difficult for radiologists and ICU personnel given the high volume of cases. This motivates the need for an automatic detection system to aid radiologists in processing these critical cases in a timely fashion while maintaining patient safety. To-date there has been very little research in this area. This paper develops a new fully automatic prototype computer-aided detection (CAD) system for detection and classification of catheters on chest radiographs using a combination of template matching, morphological processing and region growing. The preliminary evaluation was carried out on 25 cases. The prototype CAD system was able to detect ET and NG tubes with sensitivities of 73.7% and 76.5% respectively and with specificities of 91.3% and 84.0% respectively. The results from the prototype system show that it is feasible to automatically detect both catheters on chest radiographs, with the potential to significantly speed the delivery of imaging services while maintaining high accuracy.

Paper Details

Date Published: 9 March 2011
PDF: 6 pages
Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 796333 (9 March 2011); doi: 10.1117/12.878172
Show Author Affiliations
Bharath Ramakrishna, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
Matthew Brown, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
Jonathan Goldin, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
Chris Cagnon, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
Dieter Enzmann, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 7963:
Medical Imaging 2011: Computer-Aided Diagnosis
Ronald M. Summers; Bram van Ginneken, Editor(s)

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