Share Email Print
cover

Proceedings Paper

An improved automatic computer aided tube detection and labeling system on chest radiographs
Author(s): Bharath Ramakrishna; Matthew Brown; Jonathan Goldin; Christopher Cagnon; Dieter Enzmann
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Tubes like Endotracheal (ET) tube used to maintain patient's airway and the Nasogastric (NG) tube used to feed the patient and drain contents of the stomach are very commonly used in Intensive Care Units (ICU). The placement of these tubes is critical for their proper functioning and improper tube placement can even be fatal. Bedside chest radiographs are considered the quickest and safest method to check the placement of these tubes. Tertiary ICU's typically generate over 250 chest radiographs per day to confirm tube placement. This paper develops a new fully automatic prototype computer-aided detection (CAD) system for tube detection on bedside chest radiographs. The core of the CAD system is the randomized algorithm which selects tubes based on their average repeatability from seed points. The CAD algorithm is designed as a 5 stage process: Preprocessing (removing borders, histogram equalization, anisotropic filtering), Anatomy Segmentation (to identify neck, esophagus, abdomen ROI's), Seed Generation, Region Growing and Tube Selection. The preliminary evaluation was carried out on 64 cases. The prototype CAD system was able to detect ET tubes with a True Positive Rate of 0.93 and False Positive Rate of 0.02/image and NG tubes with a True Positive Rate of 0.84 and False Positive Rate of 0.02/image respectively. The results from the prototype system show that it is feasible to automatically detect both tubes on chest radiographs, with the potential to significantly speed the delivery of imaging services while maintaining high accuracy.

Paper Details

Date Published: 23 February 2012
PDF: 7 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83150R (23 February 2012); doi: 10.1117/12.911839
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)
Christopher 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. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

© SPIE. Terms of Use
Back to Top