ArticlePhysics in medicine and biology2019
Gross tumor volume segmentation for head and neck cancer radiotherapy using deep dense multi-modality network.
Article in Physics in medicine and biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Saliency-guided deep learning network for automatic tumor bed volume delineation in post-operative breast irradiation.Physics in medicine and biology · 2021Trial
- Synthetic CT imaging for pelvic and abdominal MR-only radiotherapy: Clinical validation on a 1.5T MR-Linac.Journal of applied clinical medical physics · 2026Article
- Developing a segmentation cascade deep learning network based on automated prompts.Quantitative imaging in medicine and surgery · 2026Article
- A deep learning-based computed tomography reading system for the diagnosis of lung cancer associated with cystic airspaces.Scientific reports · 2025Article
- LLM-driven multimodal target volume contouring in radiation oncology.Nature communications · 2024Article
- Artificial Intelligence in Head and Neck Surgery.Otolaryngologic clinics of North America · 2024Review
- Multi-modal segmentation with missing image data for automatic delineation of gross tumor volumes in head and neck cancers.Medical physics · 2024Article
- Head and neck tumor segmentation from [Physics in medicine and biology · 2024Article
- Adaptive radiotherapy for head and neck cancer: Pitfalls and possibilities from the radiation oncologist's point of view.Cancer medicine · 2024Review
- SwinCross: Cross-modal Swin transformer for head-and-neck tumor segmentation in PET/CT images.Medical physics · 2024Article
- Empowering PET: harnessing deep learning for improved clinical insight.European radiology experimental · 2024Review
- Multi-modal tumor segmentation methods based on deep learning: a narrative review.Quantitative imaging in medicine and surgery · 2024Review
- Fully-automated, CT-only GTV contouring for palliative head and neck radiotherapy.Scientific reports · 2023Article
- Head and Neck Cancer Segmentation in FDG PET Images: Performance Comparison of Convolutional Neural Networks and Vision Transformers.Tomography (Ann Arbor, Mich.) · 2023Article
- An overview of artificial intelligence in medical physics and radiation oncology.Journal of the National Cancer Center · 2023Review
- Deep Learning Architecture to Improve Edge Accuracy of Auto-Contouring for Head and Neck Radiotherapy.Diagnostics (Basel, Switzerland) · 2023Article
- Enhancing Radiotherapy Workflow for Head and Neck Cancer with Artificial Intelligence: A Systematic Review.Journal of personalized medicine · 2023Review
- Head and neck tumor segmentation convolutional neural network robust to missing PET/CT modalities using channel dropout.Physics in medicine and biology · 2023Article
- Towards interactive deep-learning for tumour segmentation in head and neck cancer radiotherapy.Physics and imaging in radiation oncology · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
In radiation therapy, the accurate delineation of gross tumor volume (GTV) is crucial for treatment planning. However, it is challenging for head and neck cancer (HNC) due to the morphology complexity of various organs in the head, low targets to background contrast and potential artifacts on conventional planning CT images. Thus, manual delineation of GTV on anatomical images is extremely time consuming and suffers from inter-observer variability that leads to planning uncertainty. With the wide use of PET/CT imaging in oncology, complementary functional and anatomical information can be utilized for tumor contouring and bring a significant advantage for radiation therapy planning. In this study, by taking advantage of multi-modality PET and CT images, we propose an automatic GTV segmentation framework based on deep learning for HNC. The backbone of this segmentation framework is based on 3D convolution with dense connections which enables a better information propagation and takes full advantage of the features extracted from multi-modality input images. We evaluate our proposed framework on a dataset including 250 HNC patients. Each patient receives both planning CT and PET/CT imaging before radiation therapy (RT). Manually delineated GTV contours by radiation oncologists are used as ground truth in this study. To further investigate the advantage of our proposed Dense-Net framework, we also compared with the framework using 3D U-Net which is the state-of-the-art in segmentation tasks. Meanwhile, for each frame, the performance comparison between single modality input (PET or CT image) and multi-modality input (both PET/CT) is conducted. Dice coefficient, mean surface distance (MSD), 95th-percentile Hausdorff distance (HD
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