Paper Info
Title
Locally constrained active contour: a region-based level set for ovarian cancer metastasis segmentation
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.
Year
DOI
Venue
2014
10.1117/12.2043712
Proceedings of SPIE
Keywords
Field
DocType
Ovarian Cancer Metastasis,Tumor Segmentation,Region-based Level Set,Localization
Level set,Artificial intelligence,Ovarian cancer,Active contour model,Metastasis,Computer vision,Mathematical optimization,Embedding,Pattern recognition,Sørensen–Dice coefficient,Segmentation,Initialization,Physics
Conference
Volume
ISSN
Citations 
9034
0277-786X
1
PageRank 
References 
Authors
0.38
7
5
Name
Order
Citations
PageRank
Jianfei Liu18112.98
Jianhua Yao21135110.49
Shijun Wang323922.83
Marius George Linguraru436248.94
Ronald M. Summers589386.16