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Proceedings Paper

A supervoxel-based segmentation method for prostate MR images
Author(s): Zhiqiang Tian; LiZhi Liu; Baowei Fei
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Paper Abstract

Accurate segmentation of the prostate has many applications in prostate cancer diagnosis and therapy. In this paper, we propose a “Supervoxel” based method for prostate segmentation. The prostate segmentation problem is considered as assigning a label to each supervoxel. An energy function with data and smoothness terms is used to model the labeling process. The data term estimates the likelihood of a supervoxel belongs to the prostate according to a shape feature. The geometric relationship between two neighboring supervoxels is used to construct a smoothness term. A threedimensional (3D) graph cut method is used to minimize the energy function in order to segment the prostate. A 3D level set is then used to get a smooth surface based on the output of the graph cut. The performance of the proposed segmentation algorithm was evaluated with respect to the manual segmentation ground truth. The experimental results on 12 prostate volumes showed that the proposed algorithm yields a mean Dice similarity coefficient of 86.9%±3.2%. The segmentation method can be used not only for the prostate but also for other organs.

Paper Details

Date Published: 20 March 2015
PDF: 7 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 941318 (20 March 2015); doi: 10.1117/12.2082255
Show Author Affiliations
Zhiqiang Tian, Emory Univ. (United States)
LiZhi Liu, Emory Univ. (United States)
Baowei Fei, Emory Univ. (United States)
Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)

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