ArticleMedical physics2015
A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy.
Article in Medical physics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Ensemble of LinkNet Networks for Head and Neck Tumor Segmentation.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
- Multi-modal segmentation with missing image data for automatic delineation of gross tumor volumes in head and neck cancers.Medical physics · 2024Article
- Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning.Communications medicine · 2024Article
- Head and Neck Cancer Segmentation in FDG PET Images: Performance Comparison of Convolutional Neural Networks and Vision Transformers.Tomography (Ann Arbor, Mich.) · 2023Article
- The Pattern of Metastatic Breast Cancer: A Prospective Head-to-Head Comparison of [Journal of imaging · 2023Article
- Modality preserving U-Net for segmentation of multimodal medical images.Quantitative imaging in medicine and surgery · 2023Article
- 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
- Delineation uncertainties of tumour volumes on MRI of head and neck cancer patients.Clinical and translational radiation oncology · 2022Article
- Automatic segmentation of head and neck primary tumors on MRI using a multi-view CNN.Cancer imaging : the official publication of the International Cancer Imaging Society · 2022Article
- Half-Brain Delineation for Prediction of Radiation-Induced Temporal Lobe Injury in Nasopharyngeal Carcinoma Receiving Intensity-Modulated Radiotherapy.Frontiers in oncology · 2021Article
- Fully-Automated Segmentation of Nasopharyngeal Carcinoma on Dual-Sequence MRI Using Convolutional Neural Networks.Frontiers in oncology · 2020Article
- Trimodality PET/CT/MRI and Radiotherapy: A Mini-Review.Frontiers in oncology · 2020Review
- Gross tumor volume segmentation for head and neck cancer radiotherapy using deep dense multi-modality network.Physics in medicine and biology · 2019Article
- Head and Neck Cancer Adaptive Radiation Therapy (ART): Conceptual Considerations for the Informed Clinician.Seminars in radiation oncology · 2019Review
- Segmentation of parotid glands from registered CT and MR images.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2018Article
- Fully Automated Delineation of Gross Tumor Volume for Head and Neck Cancer on PET-CT Using Deep Learning: A Dual-Center Study.Contrast media & molecular imaging · 2018Article
- Deep Deconvolutional Neural Network for Target Segmentation of Nasopharyngeal Cancer in Planning Computed Tomography Images.Frontiers in oncology · 2017Article
- The Tumor Target Segmentation of Nasopharyngeal Cancer in CT Images Based on Deep Learning Methods.Technology in cancer research & treatmentArticle
- Registration methods in radiotherapy.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation OncologyReview
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5 authors.
Funding
Abstract
purposeTo develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy.
methodsEleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to the planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation-maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the "ground truth" for quantitative evaluation.
resultsThe median multichannel segmented GTV of the primary tumor was 15.7 cm(3) (range, 6.6-44.3 cm(3)), while the PET segmented GTV was 10.2 cm(3) (range, 2.8-45.1 cm(3)). The median physician-defined GTV was 22.1 cm(3) (range, 4.2-38.4 cm(3)). The median difference between the multichannel segmented and physician-defined GTVs was -10.7%, not showing a statistically significant difference (p-value = 0.43). However, the median difference between the PET segmented and physician-defined GTVs was -19.2%, showing a statistically significant difference (p-value =0.0037). The median Dice similarity coefficient between the multichannel segmented and physician-defined GTVs was 0.75 (range, 0.55-0.84), and the median sensitivity and positive predictive value between them were 0.76 and 0.81, respectively.
conclusionsThe authors developed an automated multimodality segmentation algorithm for tumor volume delineation and validated this algorithm for head and neck cancer radiotherapy. The multichannel segmented GTV agreed well with the physician-defined GTV. The authors expect that their algorithm will improve the accuracy and consistency in target definition for radiotherapy.
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