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Proceedings Paper

Effect of knowledge-guided colon segmentation in automated detection of polyps in CT colonography
Author(s): Janne J. Nappi; Abraham H. Dachman; Peter MacEneaney; Hiroyuki Yoshida
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Paper Abstract

We developed a novel automated technique for segmenting the colonic wall in computer-aided detection of polyps in CT colonography. The technique is designed to minimize the presence of extracolonic components, such as small bowel, in the segmented colon. The colon segmentations were evaluated subjectively by four radiologists. On average, 98% of the visible colonic wall was covered by the segmentation. The amount of extracolonic components was reduced by 50% compared with our previously used anatomy-oriented colon segmentation technique, but approximately 10-15% of the segmentation still contained extracolonic components. When the technique was used with our fully automated computer-aided polyp detection scheme at a 100% by-patient detection sensitivity, the false-positive rate was reduced by 20% from 2.5 false positives to 2.0 false positives per patient. These preliminary results suggest that our new colon segmentation technique can improve the specificity of our CAD scheme significantly without degradation in the detection sensitivity.

Paper Details

Date Published: 24 April 2002
PDF: 8 pages
Proc. SPIE 4683, Medical Imaging 2002: Physiology and Function from Multidimensional Images, (24 April 2002); doi: 10.1117/12.463586
Show Author Affiliations
Janne J. Nappi, Univ. of Chicago (United States)
Abraham H. Dachman, Univ. of Chicago (United States)
Peter MacEneaney, Univ. of Chicago (United States)
Hiroyuki Yoshida, Univ. of Chicago (United States)

Published in SPIE Proceedings Vol. 4683:
Medical Imaging 2002: Physiology and Function from Multidimensional Images
Anne V. Clough; Chin-Tu Chen, Editor(s)

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