ArticleIEEE transactions on image processing : a publication of the IEEE Signal Processing Society2018
Hierarchical Vertex Regression-Based Segmentation of Head and Neck CT Images for Radiotherapy Planning.
Article in IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2018. 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.
- Nested block self-attention multiple resolution residual network for multiorgan segmentation from CT.Medical physics · 2022Article
- Deep learning-based auto segmentation using generative adversarial network on magnetic resonance images obtained for head and neck cancer patients.Journal of applied clinical medical physics · 2022Article
- Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study.Journal of medical Internet research · 2021Article
- Automatic Segmentation of Mandible from Conventional Methods to Deep Learning-A Review.Journal of personalized medicine · 2021Review
- Mandible Segmentation of Dental CBCT Scans Affected by Metal Artifacts Using Coarse-to-Fine Learning Model.Journal of personalized medicine · 2021Article
- Recurrent Convolutional Neural Networks for 3D Mandible Segmentation in Computed Tomography.Journal of personalized medicine · 2021Article
- Robust and Accurate Mandible Segmentation on Dental CBCT Scans Affected by Metal Artifacts Using a Prior Shape Model.Journal of personalized medicine · 2021Article
- Feasibility of Continual Deep Learning-Based Segmentation for Personalized Adaptive Radiation Therapy in Head and Neck Area.Cancers · 2021Article
- Automatic multi-organ segmentation in computed tomography images using hierarchical convolutional neural network.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Head and neck multi-organ auto-segmentation on CT images aided by synthetic MRI.Medical physics · 2020Article
- Artificial intelligence in radiotherapy: a technological review.Frontiers of medicine · 2020Review
- Shape constrained fully convolutional DenseNet with adversarial training for multiorgan segmentation on head and neck CT and low-field MR images.Medical physics · 2019Article
- AAR-RT - A system for auto-contouring organs at risk on CT images for radiation therapy planning: Principles, design, and large-scale evaluation on head-and-neck and thoracic cancer cases.Medical image analysis · 2019Article
- CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation.Medical image analysis · 2019Article
- Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks.Medical physics · 2018Article
- Segmentation of parotid glands from registered CT and MR images.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2018Article
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Authors and funding
6 authors.
Funding
Abstract
Segmenting organs at risk from head and neck CT images is a prerequisite for the treatment of head and neck cancer using intensity modulated radiotherapy. However, accurate and automatic segmentation of organs at risk is a challenging task due to the low contrast of soft tissue and image artifact in CT images. Shape priors have been proved effective in addressing this challenging task. However, conventional methods incorporating shape priors often suffer from sensitivity to shape initialization and also shape variations across individuals. In this paper, we propose a novel approach to incorporate shape priors into a hierarchical learning-based model. The contributions of our proposed approach are as follows: 1) a novel mechanism for critical vertices identification is proposed to identify vertices with distinctive appearances and strong consistency across different subjects; 2) a new strategy of hierarchical vertex regression is also used to gradually locate more vertices with the guidance of previously located vertices; and 3) an innovative framework of joint shape and appearance learning is further developed to capture salient shape and appearance features simultaneously. Using these innovative strategies, our proposed approach can essentially overcome drawbacks of the conventional shape-based segmentation methods. Experimental results show that our approach can achieve much better results than state-of-the-art methods.
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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.