ArticleHead and Neck Tumor Segmentation : First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings2021
Tumor Segmentation in Patients with Head and Neck Cancers Using Deep Learning Based-on Multi-modality PET/CT Images.
Article in Head and Neck Tumor Segmentation : First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Assessing the robustness and clinical evaluation of a deep-learning segmentation model for head and neck cancer.Frontiers in oncology · 2026Article
- Deep Learning Pipeline for Automated Assessment of Distances Between Tonsillar Tumors and the Internal Carotid Artery.Head & neck · 2025Article
- Automatic head and neck tumor segmentation through deep learning and Bayesian optimization on three-dimensional medical images.Computers in biology and medicine · 2025Article
- Integration of 2D/3D deep learning and radiomics for predicting lymphovascular invasion in T1-stage invasive lung adenocarcinoma: a multicenter study.Frontiers in oncology · 2025Article
- Gradient Map-Assisted Head and Neck Tumor Segmentation: A Pre-RT to Mid-RT Approach in MRI-Guided Radiotherapy.Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings · 2025Article
- Head and neck tumor segmentation from [Physics in medicine and biology · 2024Article
- Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning.Communications medicine · 2024Article
- Multi-modal tumor segmentation methods based on deep learning: a narrative review.Quantitative imaging in medicine and surgery · 2024Review
- Enhancing Radiotherapy Workflow for Head and Neck Cancer with Artificial Intelligence: A Systematic Review.Journal of personalized medicine · 2023Review
- Application of simultaneous uncertainty quantification for image segmentation with probabilistic deep learning: Performance benchmarking of oropharyngeal cancer target delineation as a use-case.medRxiv : the preprint server for health sciences · 2023Article
- Segmentation stability of human head and neck cancer medical images for radiotherapy applications under de-identification conditions: Benchmarking data sharing and artificial intelligence use-cases.Frontiers in oncology · 2023Article
- Towards interactive deep-learning for tumour segmentation in head and neck cancer radiotherapy.Physics and imaging in radiation oncology · 2023Article
- Rapid artificial intelligence solutions in a pandemic-The COVID-19-20 Lung CT Lesion Segmentation Challenge.Medical image analysis · 2022Article
- Evaluation of deep learning-based multiparametric MRI oropharyngeal primary tumor auto-segmentation and investigation of input channel effects: Results from a prospective imaging registry.Clinical and translational radiation oncology · 2022Article
- Deep-learning-based generation of synthetic 6-minute MRI from 2-minute MRI for use in head and neck cancer radiotherapy.Frontiers in oncology · 2022Article
- Progression Free Survival Prediction for Head and Neck Cancer Using Deep Learning Based on Clinical and PET/CT Imaging Data.Head and neck tumor segmentation and outcome prediction : second challenge, HECKTOR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings. Head and Neck Tumor Segmentation Challenge (2nd : 2021 ... · 2022Article
- Head and Neck Cancer Primary Tumor Auto Segmentation Using Model Ensembling of Deep Learning in PET/CT Images.Head and neck tumor segmentation and outcome prediction : second challenge, HECKTOR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings. Head and Neck Tumor Segmentation Challenge (2nd : 2021 ... · 2022Article
- Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge.Research square · 2021Article
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5 authors.
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Abstract
Segmentation of head and neck cancer (HNC) primary tumors onmedical images is an essential, yet labor-intensive, aspect of radiotherapy.PET/CT imaging offers a unique ability to capture metabolic and anatomicinformation, which is invaluable for tumor detection and border definition. Anautomatic segmentation tool that could leverage the dual streams of informationfrom PET and CT imaging simultaneously, could substantially propel HNCradiotherapy workflows forward. Herein, we leverage a multi-institutionalPET/CT dataset of 201 HNC patients, as part of the MICCAI segmentationchallenge, to develop novel deep learning architectures for primary tumor auto-segmentation for HNC patients. We preprocess PET/CT images by normalizingintensities and applying data augmentation to mitigate overfitting. Both 2D and3D convolutional neural networks based on the U-net architecture, which wereoptimized with a model loss function based on a combination of dice similaritycoefficient (DSC) and binary cross entropy, were implemented. The median andmean DSC values comparing the predicted tumor segmentation with the groundtruth achieved by the models through 5-fold cross validation are 0.79 and 0.69for the 3D model, respectively, and 0.79 and 0.67 for the 2D model, respec-tively. These promising results show potential to provide an automatic, accurate,and efficient approach for primary tumor auto-segmentation to improve theclinical practice of HNC treatment.
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