ArticlePhysics in medicine and biology2022
Multi-organ auto-delineation in head-and-neck MRI for radiation therapy using regional convolutional neural network.
Article in Physics in medicine and biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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13 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis.Biomedical engineering online · 2023Pooled it
- 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
- Consistency evaluation and performance optimization of deep learning-based auto-contouring for nasopharyngeal carcinoma.Scientific reports · 2025Article
- Large language model-augmented learning for auto-delineation of treatment targets in head-and-neck cancer radiotherapy.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2025Article
- Large Language Model-Augmented Auto-Delineation of Treatment Target Volume in Radiation Therapy.ArXiv · 2024Article
- Deep learning segmentation of organs-at-risk with integration into clinical workflow for pediatric brain radiotherapy.Journal of applied clinical medical physics · 2024Article
- The Use of MR-Guided Radiation Therapy for Head and Neck Cancer and Recommended Reporting Guidance.Seminars in radiation oncology · 2024Review
- 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
- Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023Review
- Enhancing Radiotherapy Workflow for Head and Neck Cancer with Artificial Intelligence: A Systematic Review.Journal of personalized medicine · 2023Review
- Automatic segmentation and quantification of the optic nerve on MRI using a 3D U-Net.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Article
- Deep learning-based two-step organs at risk auto-segmentation model for brachytherapy planning in parotid gland carcinoma.Journal of contemporary brachytherapy · 2022Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
Magnetic resonance imaging (MRI) allows accurate and reliable organ delineation for many disease sites in radiation therapy because MRI is able to offer superb soft-tissue contrast. Manual organ-at-risk delineation is labor-intensive and time-consuming. This study aims to develop a deep-learning-based automated multi-organ segmentation method to release the labor and accelerate the treatment planning process for head-and-neck (HN) cancer radiotherapy. A novel regional convolutional neural network (R-CNN) architecture, namely, mask scoring R-CNN, has been developed in this study. In the proposed model, a deep attention feature pyramid network is used as a backbone to extract the coarse features given by MRI, followed by feature refinement using R-CNN. The final segmentation is obtained through mask and mask scoring networks taking those refined feature maps as input. With the mask scoring mechanism incorporated into conventional mask supervision, the classification error can be highly minimized in conventional mask R-CNN architecture. A cohort of 60 HN cancer patients receiving external beam radiation therapy was used for experimental validation. Five-fold cross-validation was performed for the assessment of our proposed method. The Dice similarity coefficients of 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 were 0.89 ± 0.06, 0.68 ± 0.14/0.68 ± 0.18, 0.89 ± 0.07/0.89 ± 0.05, 0.90 ± 0.07, 0.67 ± 0.18/0.67 ± 0.10, 0.82 ± 0.10, 0.61 ± 0.14, 0.67 ± 0.11/0.68 ± 0.11, 0.92 ± 0.07, 0.85 ± 0.06/0.86 ± 0.05, 0.80 ± 0.13, and 0.77 ± 0.15, respectively. After the model training, all OARs can be segmented within 1 min.
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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.