ArticleJournal of applied clinical medical physics2022
Deep learning-based auto segmentation using generative adversarial network on magnetic resonance images obtained for head and neck cancer patients.
Article in Journal of applied clinical medical physics, 2022. 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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16 citing papers in PubMed, 20 citations in OpenAlex.
- Automated MR-only radiotherapy outperforms CT-based radiotherapy and decreases hands-on time for head-and-neck cancer treatment.Clinical and translational radiation oncology · 2026Article
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- The Evolving Role of Radiation Therapy Technologists in Head and Neck Cancer: A Narrative Review and Operational Framework.Journal of imaging · 2026Review
- Multi-modality brain tumor segmentation using dual-attention generative adversarial network.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2026Article
- Artificial intelligence in head and neck cancer: a bibliometric and visualization analysis (1995-2025).Discover oncology · 2025Article
- Assessing the value of artificial intelligence-based image analysis for pre-operative surgical planning of neck dissections and iENE detection in head and neck cancer patients.Discover oncology · 2025Article
- Automatic segmentation for magnetic resonance imaging guided individual elective lymph node irradiation in head and neck cancer patients.Physics and imaging in radiation oncology · 2024Article
- The InterVision Framework: An Enhanced Fine-Tuning Deep Learning Strategy for Auto-Segmentation in Head and Neck.Journal of personalized medicine · 2024Article
- Artificial intelligence to unlock real-world evidence in clinical oncology: A primer on recent advances.Cancer medicine · 2024Review
- Essentially unedited deep-learning-based OARs are suitable for rigorous oropharyngeal and laryngeal cancer treatment planning.Journal of applied clinical medical physics · 2024Article
- Clinical acceptance and dosimetric impact of automatically delineated elective target and organs at risk for head and neck MR-Linac patients.Frontiers in oncology · 2024Article
- Auto-segmentation of neck nodal metastases using self-distilled masked image transformer on longitudinal MR images.BJR artificial intelligence · 2024Article
- The Use of MR-Guided Radiation Therapy for Head and Neck Cancer and Recommended Reporting Guidance.Seminars in radiation oncology · 2024Review
- Review
- Article
- 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
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Authors and funding
7 authors at 5 institutions in 1 country.
Funding
Abstract
purposeAdaptive radiotherapy requires auto-segmentation in patients with head and neck (HN) cancer. In the current study, we propose an auto-segmentation model using a generative adversarial network (GAN) on magnetic resonance (MR) images of HN cancer for MR-guided radiotherapy (MRgRT). MATERIAL AND
methodsIn the current study, we used a dataset from the American Association of Physicists in Medicine MRI Auto-Contouring (RT-MAC) Grand Challenge 2019. Specifically, eight structures in the MR images of HN region, namely submandibular glands, lymph node level II and level III, and parotid glands, were segmented with the deep learning models using a GAN and a fully convolutional network with a U-net. These images were compared with the clinically used atlas-based segmentation.
resultsThe mean Dice similarity coefficient (DSC) of the U-net and GAN models was significantly higher than that of the atlas-based method for all the structures (p < 0.05). Specifically, the maximum Hausdorff distance (HD) was significantly lower than that in the atlas method (p < 0.05). Comparing the 2.5D and 3D U-nets, the 3D U-net was superior in segmenting the organs at risk (OAR) for HN patients. The DSC was highest for 0.75-0.85, and the HD was lowest within 5.4 mm of the 2.5D GAN model in all the OARs.
conclusionsIn the current study, we investigated the auto-segmentation of the OAR for HN patients using U-net and GAN models on MR images. Our proposed model is potentially valuable for improving the efficiency of HN RT treatment planning.
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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.