ArticleProceedings of SPIE--the International Society for Optical Engineering
3D Transrectal Ultrasound (TRUS) Prostate Segmentation Based on Optimal Feature Learning Framework.
Article in Proceedings of SPIE--the International Society for Optical Engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- 3D EAGAN: 3D edge-aware attention generative adversarial network for prostate segmentation in transrectal ultrasound images.Quantitative imaging in medicine and surgery · 2024Article
- Current Approach to Complications and Difficulties during Transrectal Ultrasound-Guided Prostate Biopsies.Journal of clinical medicine · 2024Review
- Domain generalization for prostate segmentation in transrectal ultrasound images: A multi-center study.Medical image analysis · 2022Article
- Artificial Intelligence in Radiation Therapy.IEEE transactions on radiation and plasma medical sciences · 2022Article
- Catheter position prediction using deep-learning-based multi-atlas registration for high-dose rate prostate brachytherapy.Medical physics · 2021Article
- Deep learning in medical image registration: a review.Physics in medicine and biology · 2020Review
- Ultrasound prostate segmentation based on multidirectional deeply supervised V-Net.Medical physics · 2019Article
- Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation.Medical physics · 2019Article
- Machine learning for medical ultrasound: status, methods, and future opportunities.Abdominal radiology (New York) · 2018Review
- A Denoising Algorithm for CT Image Using Low-rank Sparse Coding.Proceedings of SPIE--the International Society for Optical Engineering · 2018Article
- Improving Image Quality of Cone-Beam CT Using Alternating Regression Forest.Proceedings of SPIE--the International Society for Optical Engineering · 2018Article
- A random walk-based segmentation framework for 3D ultrasound images of the prostate.Medical physics · 2017Article
- Pseudo CT Estimation from MRI Using Patch-based Random Forest.Proceedings of SPIE--the International Society for Optical Engineering · 2017Article
- A Patch-based CBCT Scatter Artifact Correction Using Prior CT.Proceedings of SPIE--the International Society for Optical Engineering · 2017Article
- Dosimetric study on learning-based cone-beam CT correction in adaptive radiation therapy.Medical dosimetry : official journal of the American Association of Medical DosimetristsArticle
- A review of artificial intelligence in prostate cancer detection on imaging.Therapeutic advances in urologyReview
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
We propose a 3D prostate segmentation method for transrectal ultrasound (TRUS) images, which is based on patch-based feature learning framework. Patient-specific anatomical features are extracted from aligned training images and adopted as signatures for each voxel. The most robust and informative features are identified by the feature selection process to train the kernel support vector machine (KSVM). The well-trained SVM was used to localize the prostate of the new patient. Our segmentation technique was validated with a clinical study of 10 patients. The accuracy of our approach was assessed using the manual segmentations (gold standard). The mean volume Dice overlap coefficient was 89.7%. In this study, we have developed a new prostate segmentation approach based on the optimal feature learning framework, demonstrated its clinical feasibility, and validated its accuracy with manual segmentations.
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