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Proceedings Paper

Automatic lung nodule detection in thick slice CT: a comparative study of different gating schemes in CAD
Author(s): Pandu Devarakota; M. S. Dinesh; Pragnya Maduskar; Siddharth Vikal; Laks Raghupathi; Marcos Salganicoff
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Paper Abstract

Common chest CT clinical workflows for detecting lung nodules use a large slice thickness protocol (typically 5 mm). However, most existing CAD studies are performed on a thin slice data (0.3-2 mm) available on state-of-the art scanners. A major challenge for the widespread clinical use of Lung CAD is the concurrent availability of both thick and thin resolutions for use by radiologist and CAD respectively. Having both slice thickness reconstructions is not always possible based on the availability of scanner technologies, acquisition parameters chosen at remote site, and transmission and archiving constraints that may make transmission and storage of large data impracticable. However, applying current thin-slice CAD algorithms on thick slice cases outside their designed acquisition parameters may result in degradation of sensitivity and high false-positive rate making them clinically unacceptable. Therefore a CAD system that can handle thicker slice acquisitions is desirable to address those situations. In this paper, we propose a CAD system which works directly on thick slice scans. We first propose a multi-stage classifier based CAD system for detecting lung nodules in such data. Furthermore, we propose different gating systems adapted for thick slice scans. The proposed gating schemes are based on: 1. wall-attached and non wall-attached. 2. central and non-central region. These gating schemes can be used independently or combined as well. Finally, we present prototype1 results showing significant improvement of CAD sensitivity at much better false positive rate on thick-slice CT images are presented.

Paper Details

Date Published: 4 March 2011
PDF: 8 pages
Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79630E (4 March 2011); doi: 10.1117/12.878404
Show Author Affiliations
Pandu Devarakota, Siemens Information Systems Ltd. (India)
M. S. Dinesh, Siemens Information Systems Ltd. (India)
Pragnya Maduskar, Siemens Information Systems Ltd. (India)
Siddharth Vikal, Siemens Information Systems Ltd. (India)
Laks Raghupathi, Siemens Information Systems Ltd. (India)
Marcos Salganicoff, Siemens Healthcare, Inc. (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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