ArticleProceedings of SPIE--the International Society for Optical Engineering2015
A supervoxel-based segmentation method for prostate MR images.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A NOVEL IMAGE-SPECIFIC TRANSFER APPROACH FOR PROSTATE SEGMENTATION IN MR IMAGES.Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference) · 2018Article
- Computer-aided diagnosis of prostate cancer with MRI.Current opinion in biomedical engineering · 2017Article
- Molecular imaging and fusion targeted biopsy of the prostate.Clinical and translational imaging · 2017Article
- A supervoxel-based segmentation method for prostate MR images.Medical physics · 2017Article
- Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications.Academic radiology · 2016Review
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Authors and funding
3 authors.
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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 three-dimensional (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.
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