ArticlePhysics in medicine and biology2021
Head-and-neck organs-at-risk auto-delineation using dual pyramid networks for CBCT-guided adaptive radiotherapy.
Article in Physics in medicine and biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 44 citations in OpenAlex.
- Improving deep learning organ segmentation accuracy in cone-beam CT-guided radiotherapy using a robust scatter suppression method.Physics and imaging in radiation oncology · 2026Article
- Automatic adaptive radiotherapy triggering based on CBCT using deep learning for esophageal cancer underwent volumetric modulated arc therapy.Journal of applied clinical medical physics · 2026Article
- Refining imaging parameters for dual-energy cone-beam computed tomography in image-guided radiation therapy.Journal of applied clinical medical physics · 2026Article
- Artificial intelligence for treatment delivery: image-guided radiotherapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Evaluation of artificial intelligence-based autosegmentation for a high-performance cone-beam computed tomography imaging system in the pelvic region.Physics and imaging in radiation oncology · 2025Article
- Interobserver Variability in a Spanish Society of Radiation Oncology (SEOR) Head and Neck Course. Is Current Contouring Training Sufficient?Advances in radiation oncology · 2024Article
- Parotid Gland Segmentation Using Purely Transformer-Based U-Shaped Network and Multimodal MRI.Annals of biomedical engineering · 2024Article
- Clinical Experience With an Offline Adaptive Radiation Therapy Head and Neck Program: Dosimetric Benefits and Opportunities for Patient Selection.International journal of radiation oncology, biology, physics · 2024Article
- Progressive auto-segmentation for cone-beam computed tomography-based online adaptive radiotherapy.Physics and imaging in radiation oncology · 2024Article
- The Use of MR-Guided Radiation Therapy for Head and Neck Cancer and Recommended Reporting Guidance.Seminars in radiation oncology · 2024Review
- A novel comprehensive metric balancing imaging dose and setup accuracy in image-guided radiotherapy: concept proposal and clinical validation.Frontiers in oncology · 2024Article
- Young oncologists benefit more than experts from deep learning-based organs-at-risk contouring modeling in nasopharyngeal carcinoma radiotherapy: A multi-institution clinical study exploring working experience and institute group style factor.Clinical and translational radiation oncology · 2023Article
- Segmentation by test-time optimization for CBCT-based adaptive radiation therapy.Medical physics · 2023Article
- Feasibility of a deep-learning based anatomical region labeling tool for Cone-Beam Computed Tomography scans in radiotherapy.Physics and imaging in radiation oncology · 2023Article
- Automated lung tumor delineation on positron emission tomography/computed tomography via a hybrid regional network.Medical physics · 2023Article
- Multi-scale organs image segmentation method improved by squeeze-and-attention based on partially supervised learning.International journal of computer assisted radiology and surgery · 2022Article
- Multi-organ auto-delineation in head-and-neck MRI for radiation therapy using regional convolutional neural network.Physics in medicine and biology · 2022Article
- Artificial intelligence in image-guided radiotherapy: a review of treatment target localization.Quantitative imaging in medicine and surgery · 2021Review
- Deep learning-based motion tracking using ultrasound images.Medical physics · 2021Article
- Synthetic CT-aided multiorgan segmentation for CBCT-guided adaptive pancreatic radiotherapy.Medical physics · 2021Article
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
Organ-at-risk (OAR) delineation is a key step for cone-beam CT (CBCT) based adaptive radiotherapy planning that can be a time-consuming, labor-intensive, and subject-to-variability process. We aim to develop a fully automated approach aided by synthetic MRI for rapid and accurate CBCT multi-organ contouring in head-and-neck (HN) cancer patients. MRI has superb soft-tissue contrasts, while CBCT offers bony-structure contrasts. Using the complementary information provided by MRI and CBCT is expected to enable accurate multi-organ segmentation in HN cancer patients. In our proposed method, MR images are firstly synthesized using a pre-trained cycle-consistent generative adversarial network given CBCT. The features of CBCT and synthetic MRI (sMRI) are then extracted using dual pyramid networks for final delineation of organs. CBCT images and their corresponding manual contours were used as pairs to train and test the proposed model. Quantitative metrics including Dice similarity coefficient (DSC), Hausdorff distance 95% (HD95), mean surface distance, and residual mean square distance (RMS) were used to evaluate the proposed method. The proposed method was evaluated on a cohort of 65 HN cancer patients. CBCT images were collected from those patients who received proton therapy. Overall, DSC values of 0.87 ± 0.03, 0.79 ± 0.10/0.79 ± 0.11, 0.89 ± 0.08/0.89 ± 0.07, 0.90 ± 0.08, 0.75 ± 0.06/0.77 ± 0.06, 0.86 ± 0.13, 0.66 ± 0.14, 0.78 ± 0.05/0.77 ± 0.04, 0.96 ± 0.04, 0.89 ± 0.04/0.89 ± 0.04, 0.83 ± 0.02, and 0.84 ± 0.07 for commonly used OARs for treatment planning including brain stem, left/right cochlea, left/right eye, larynx, left/right lens, mandible, optic chiasm, left/right optic nerve, oral cavity, left/right parotid, pharynx, and spinal cord, respectively, were achieved. This study provides a rapid and accurate OAR auto-delineation approach, which can be used for adaptive radiation therapy.
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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.