期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 35, 期 3, 页码 791-801出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2015.2496296
关键词
3D graph cuts; active contour model; superpixel; magnetic resonance imaging (MRI); prostate segmentation
类别
资金
- NIH grants [CA156775, CA176684]
- Georgia Cancer Coalition Distinguished Clinicians and Scientists Award
- Emory Molecular and Translational Imaging Center [CA128301]
- National Natural Science Foundation of China [81372274]
- Natural Science Foundation of Guangdong Province [2014A030313033]
This paper proposes a method for segmenting the prostate on magnetic resonance (MR) images. A superpixel-based 3D graph cut algorithm is proposed to obtain the prostate surface. Instead of pixels, superpixels are considered as the basic processing units to construct a 3D superpixel-based graph. The superpixels are labeled as the prostate or background by minimizing an energy function using graph cut based on the 3D superpixel-based graph. To construct the energy function, we proposed a superpixel-based shape data term, an appearance data term, and two superpixel-based smoothness terms. The proposed superpixel-based terms provide the effectiveness and robustness for the segmentation of the prostate. The segmentation result of graph cuts is used as an initialization of a 3D active contour model to overcome the drawback of the graph cut. The result of 3D active contour model is then used to update the shape model and appearance model of the graph cut. Iterations of the 3D graph cut and 3D active contour model have the ability to jump out of local minima and obtain a smooth prostate surface. On our 43 MR volumes, the proposed method yields a mean Dice ratio of 89.3 +/- 1.9%. On PROMISE12 test data set, our method was ranked at the second place; the mean Dice ratio and standard deviation is 87.0 +/- 3.2%. The experimental results show that the proposed method outperforms several state-of-the-art prostate MRI segmentation methods.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据