ArticleIEEE transactions on medical imaging2016
Superpixel-Based Segmentation for 3D Prostate MR Images.
Article in IEEE transactions on medical imaging, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Toward New Assessment in Sarcoma Identification and Grading Using Artificial Intelligence Techniques.Diagnostics (Basel, Switzerland) · 2025Article
- Generative Artificial Intelligence in Prostate Cancer Imaging.Balkan medical journal · 2025Review
- Deep learning-aided 3D proxy-bridged region-growing framework for multi-organ segmentation.Scientific reports · 2024Article
- Deep learning-based dominant index lesion segmentation for MR-guided radiation therapy of prostate cancer.Medical physics · 2023Article
- LcmUNet: A Lightweight Network Combining CNN and MLP for Real-Time Medical Image Segmentation.Bioengineering (Basel, Switzerland) · 2023Article
- Diagnosis of Prostate Cancer Using GLCM Enabled KNN Technique by Analyzing MRI Images.BioMed research international · 2023Article
- EDLDR: An Ensemble Deep Learning Technique for Detection and Classification of Diabetic Retinopathy.Diagnostics (Basel, Switzerland) · 2022Article
- Automatic Aortic Valve Cusps Segmentation from CT Images Based on the Cascading Multiple Deep Neural Networks.Journal of imaging · 2022Article
- Supervised Domain Adaptation for Automated Semantic Segmentation of the Atrial Cavity.Entropy (Basel, Switzerland) · 2021Article
- MSU-Net: Multi-Scale U-Net for 2D Medical Image Segmentation.Frontiers in genetics · 2021Article
- CTumorGAN: a unified framework for automatic computed tomography tumor segmentation.European journal of nuclear medicine and molecular imaging · 2020Article
- Graph-convolutional-network-based interactive prostate segmentation in MR images.Medical physics · 2020Article
- Superpixel-based deep convolutional neural networks and active contour model for automatic prostate segmentation on 3D MRI scans.Medical & biological engineering & computing · 2020Article
- Investigating the Performance of Generative Adversarial Networks for Prostate Tissue Detection and Segmentation.Journal of imaging · 2020Article
- SLIC-supervoxels-based response evaluation of osteosarcoma treated with neoadjuvant chemotherapy using multi-parametric MR imaging.European radiology · 2020Article
- Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state-of-art applications.Medical physics · 2020Review
- Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation.IEEE transactions on medical imaging · 2020Article
- Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC.Brain sciences · 2020Article
- STRAINet: Spatially Varying sTochastic Residual AdversarIal Networks for MRI Pelvic Organ Segmentation.IEEE transactions on neural networks and learning systems · 2019Article
- 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
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.