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

Jianfei Liu; Jianhua Yao; Shijun Wang; Marius George Linguraru; Ronald M. Summers

doi:10.1117/12.2043712

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21 March 2014 Locally constrained active contour: a region-based level set for ovarian cancer metastasis segmentation

Jianfei Liu, Jianhua Yao, Shijun Wang, Marius George Linguraru, Ronald M. Summers

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Proceedings Volume 9034, Medical Imaging 2014: Image Processing; 903404 (2014) https://doi.org/10.1117/12.2043712
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

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.

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Jianfei Liu, Jianhua Yao, Shijun Wang, Marius George Linguraru, and Ronald M. Summers "Locally constrained active contour: a region-based level set for ovarian cancer metastasis segmentation", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 903404 (21 March 2014); https://doi.org/10.1117/12.2043712

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