ArticleCancer imaging : the official publication of the International Cancer Imaging Society2022
Automatic segmentation of head and neck primary tumors on MRI using a multi-view CNN.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 27 citations in OpenAlex.
- Deep learning-based independent lymph node segmentation in esophageal cancer: a precise and efficient approach for radiotherapy planning.Quantitative imaging in medicine and surgery · 2026Article
- Acute deep neck infection MRI: deep learning segmentation and clinical relevance of retropharyngeal edema volume.European radiology experimental · 2026Article
- Deep learning [Clinical and translational radiation oncology · 2026Article
- A method for sensitivity analysis of automatic contouring algorithms across different contrast weightings using synthetic magnetic resonance imaging.Physics and imaging in radiation oncology · 2025Article
- Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery.BMC medical imaging · 2025Article
- Development and validation of MRI-derived deep learning score for non-invasive prediction of PD-L1 expression and prognostic stratification in head and neck squamous cell carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- A Method for Sensitivity Analysis of Automatic Contouring Algorithms Across Different MRI Contrast Weightings Using SyntheticMR.medRxiv : the preprint server for health sciences · 2025Article
- Head and Neck Tumor Segmentation Using Pre-RT MRI Scans and Cascaded DualUNet.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
- A Coarse-to-Fine Framework for Mid-Radiotherapy Head and Neck Cancer MRI 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
- 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
- Automatic Segmentation of Heschl Gyrus and Planum Temporale by MRICloud.Otology & neurotology open · 2024Article
- Auto-segmentation of neck nodal metastases using self-distilled masked image transformer on longitudinal MR images.BJR artificial intelligence · 2024Article
- Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning.European radiology · 2023Article
- Deep Learning for Automated Elective Lymph Node Level Segmentation for Head and Neck Cancer Radiotherapy.Cancers · 2022Article
- Strategies for tackling the class imbalance problem of oropharyngeal primary tumor segmentation on magnetic resonance imaging.Physics and imaging in radiation oncology · 2022Article
- Magnetic Resonance Guided Radiotherapy for Head and Neck Cancers.Journal of clinical medicine · 2022Review
- Artificial intelligence assists precision medicine in cancer treatment.Frontiers in oncology · 2022Review
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundAccurate segmentation of head and neck squamous cell cancer (HNSCC) is important for radiotherapy treatment planning. Manual segmentation of these tumors is time-consuming and vulnerable to inconsistencies between experts, especially in the complex head and neck region. The aim of this study is to introduce and evaluate an automatic segmentation pipeline for HNSCC using a multi-view CNN (MV-CNN).
methodsThe dataset included 220 patients with primary HNSCC and availability of T1-weighted, STIR and optionally contrast-enhanced T1-weighted MR images together with a manual reference segmentation of the primary tumor by an expert. A T1-weighted standard space of the head and neck region was created to register all MRI sequences to. An MV-CNN was trained with these three MRI sequences and evaluated in terms of volumetric and spatial performance in a cross-validation by measuring intra-class correlation (ICC) and dice similarity score (DSC), respectively.
resultsThe average manual segmented primary tumor volume was 11.8±6.70 cm
conclusionAn automatic segmentation pipeline was evaluated for primary HNSCC on MRI. The MV-CNN produced reasonable segmentation results, especially on large tumors, but overestimation decreased overall performance. In further research, the focus should be on decreasing false positives and make it valuable in 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.