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Proceedings Paper

CT liver volumetry using geodesic active contour segmentation with a level-set algorithm
Author(s): Kenji Suzuki; Mark L. Epstein; Ryan Kohlbrenner; Ademola Obajuluwa; Jianwu Xu; Masatoshi Hori; Richard Baron
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Paper Abstract

Automatic liver segmentation on CT images is challenging because the liver often abuts other organs of a similar density. Our purpose was to develop an accurate automated liver segmentation scheme for measuring liver volumes. We developed an automated volumetry scheme for the liver in CT based on a 5 step schema. First, an anisotropic smoothing filter was applied to portal-venous phase CT images to remove noise while preserving the liver structure, followed by an edge enhancer to enhance the liver boundary. By using the boundary-enhanced image as a speed function, a fastmarching algorithm generated an initial surface that roughly estimated the liver shape. A geodesic-active-contour segmentation algorithm coupled with level-set contour-evolution refined the initial surface so as to more precisely fit the liver boundary. The liver volume was calculated based on the refined liver surface. Hepatic CT scans of eighteen prospective liver donors were obtained under a liver transplant protocol with a multi-detector CT system. Automated liver volumes obtained were compared with those manually traced by a radiologist, used as "gold standard." The mean liver volume obtained with our scheme was 1,520 cc, whereas the mean manual volume was 1,486 cc, with the mean absolute difference of 104 cc (7.0%). CT liver volumetrics based on an automated scheme agreed excellently with "goldstandard" manual volumetrics (intra-class correlation coefficient was 0.95) with no statistically significant difference (p(F≤f)=0.32), and required substantially less completion time. Our automated scheme provides an efficient and accurate way of measuring liver volumes.

Paper Details

Date Published: 9 March 2010
PDF: 6 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76240R (9 March 2010); doi: 10.1117/12.843950
Show Author Affiliations
Kenji Suzuki, The Univ. of Chicago (United States)
Mark L. Epstein, The Univ. of Chicago (United States)
Ryan Kohlbrenner, The Univ. of Chicago (United States)
Ademola Obajuluwa, The Univ. of Chicago (United States)
Jianwu Xu, The Univ. of Chicago (United States)
Masatoshi Hori, The Univ. of Chicago (United States)
Richard Baron, The Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 7624:
Medical Imaging 2010: Computer-Aided Diagnosis
Nico Karssemeijer; Ronald M. Summers, Editor(s)

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