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Proceedings Paper

Locally constrained active contour: a region-based level set for ovarian cancer metastasis segmentation
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Paper Abstract

Accurate segmentation of ovarian cancer metastases is clinically useful to evaluate tumor growth and determine follow-up treatment. We present a region-based level set algorithm with localization constraints to segment ovarian cancer metastases. Our approach is established on a representative region-based level set, Chan-Vese model, in which an active contour is driven by region competition. To reduce over-segmentation, we constrain the level set propagation within a narrow image band by embedding a dynamic localization function. The metastasis intensity prior is also estimated from image regions within the level set initialization. The localization function and intensity prior force the level set to stop at the desired metastasis boundaries. Our approach was validated on 19 ovarian cancer metastases with radiologist-labeled ground-truth on contrast-enhanced CT scans from 15 patients. The comparison between our algorithm and geodesic active contour indicated that the volume overlap was 75±10% vs. 56±6%, the Dice coefficient was 83±8% vs. 63±8%, and the average surface distance was 2.2±0.6mm vs. 4.4±0.9mm. Experimental results demonstrated that our algorithm outperformed traditional level set algorithms.

Paper Details

Date Published: 21 March 2014
PDF: 6 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 903404 (21 March 2014); doi: 10.1117/12.2043712
Show Author Affiliations
Jianfei Liu, National Institutes of Health Clinical Ctr. (United States)
Jianhua Yao, National Institutes of Health Clinical Ctr. (United States)
Shijun Wang, National Institutes of Health Clinical Ctr. (United States)
Marius George Linguraru, Children's National Medical Ctr. (United States)
Ronald M. Summers, National Institutes of Health Clinical Ctr. (United States)


Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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