ArticleMedical physics2017
A supervoxel-based segmentation method for prostate MR images.
Article in Medical physics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 32 citations in OpenAlex.
- A combined deformable model and medical transformer algorithm for medical image segmentation.Medical & biological engineering & computing · 2023Article
- Effect of Metabolic Syndrome on Anatomy and Function of the Lower Urinary Tract Assessed on MRI.Urology · 2022Article
- Superpixel-based deep convolutional neural networks and active contour model for automatic prostate segmentation on 3D MRI scans.Medical & biological engineering & computing · 2020Article
- Graph-convolutional-network-based interactive prostate segmentation in MR images.Medical physics · 2020Article
- A semiautomatic approach for prostate segmentation in MR images using local texture classification and statistical shape modeling.Proceedings of SPIE--the International Society for Optical Engineering · 2019Article
- Computer-aided diagnosis of prostate cancer with MRI.Current opinion in biomedical engineering · 2017Article
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
Abstract
purposeSegmentation of the prostate on MR images has many applications in prostate cancer management. In this work, we propose a supervoxel-based segmentation method for prostate MR images.
methodsA supervoxel is a set of pixels that have similar intensities, locations, and textures in a 3D image volume. The prostate segmentation problem is considered as assigning a binary label to each supervoxel, which is either the prostate or background. A supervoxel-based energy function with data and smoothness terms is used to model the label. The data term estimates the likelihood of a supervoxel belonging to the prostate by using a supervoxel-based shape feature. The geometric relationship between two neighboring supervoxels is used to build the smoothness term. The 3D graph cut is used to minimize the energy function to get the labels of the supervoxels, which yields the prostate segmentation. A 3D active contour model is then used to get a smooth surface by using the output of the graph cut as an initialization. The performance of the proposed algorithm was evaluated on 30 in-house MR image data and PROMISE12 dataset.
resultsThe mean Dice similarity coefficients are 87.2 ± 2.3% and 88.2 ± 2.8% for our 30 in-house MR volumes and the PROMISE12 dataset, respectively. The proposed segmentation method yields a satisfactory result for prostate MR images.
conclusionThe proposed supervoxel-based method can accurately segment prostate MR images and can have a variety of application in prostate cancer diagnosis and therapy.
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