ArticlePloS one2013
PCG-cut: graph driven segmentation of the prostate central gland.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
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- [MS-DTNet: an efficient model combining multi-scale convolution and dual-tower attention for detecting abnormal heart sounds].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- TA-Net: Topology-Aware Network for Gland Segmentation.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision · 2022Article
- Ultrasound prostate segmentation based on multidirectional deeply supervised V-Net.Medical physics · 2019Article
- Multi-Organ Gland Segmentation Using Deep Learning.Frontiers in medicine · 2019Article
- Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings.Journal of magnetic resonance imaging : JMRI · 2018Article
- Algorithm guided outlining of 105 pancreatic cancer liver metastases in Ultrasound.Scientific reports · 2017Article
- Interactive Outlining of Pancreatic Cancer Liver Metastases in Ultrasound Images.Scientific reports · 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
- Article
- Article
- Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications.Academic radiology · 2016Review
- Superpixel-Based Segmentation for 3D Prostate MR Images.IEEE transactions on medical imaging · 2016Article
- Interactive Volumetry Of Liver Ablation Zones.Scientific reports · 2015Article
- A supervoxel-based segmentation method for prostate MR images.Proceedings of SPIE--the International Society for Optical Engineering · 2015Article
- Refinement-cut: user-guided segmentation algorithm for translational science.Scientific reports · 2014Article
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Abstract
Prostate cancer is the most abundant cancer in men, with over 200,000 expected new cases and around 28,000 deaths in 2012 in the US alone. In this study, the segmentation results for the prostate central gland (PCG) in MR scans are presented. The aim of this research study is to apply a graph-based algorithm to automated segmentation (i.e. delineation) of organ limits for the prostate central gland. The ultimate goal is to apply automated segmentation approach to facilitate efficient MR-guided biopsy and radiation treatment planning. The automated segmentation algorithm used is graph-driven based on a spherical template. Therefore, rays are sent through the surface points of a polyhedron to sample the graph's nodes. After graph construction--which only requires the center of the polyhedron defined by the user and located inside the prostate center gland--the minimal cost closed set on the graph is computed via a polynomial time s-t-cut, which results in the segmentation of the prostate center gland's boundaries and volume. The algorithm has been realized as a C++ module within the medical research platform MeVisLab and the ground truth of the central gland boundaries were manually extracted by clinical experts (interventional radiologists) with several years of experience in prostate treatment. For evaluation the automated segmentations of the proposed scheme have been compared with the manual segmentations, yielding an average Dice Similarity Coefficient (DSC) of 78.94 ± 10.85%.
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