ArticleRadiation oncology (London, England)2020
Convolutional neural networks for head and neck tumor segmentation on 7-channel multiparametric MRI: a leave-one-out analysis.
Article in Radiation oncology (London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- FOCUS-DWI improves prostate cancer detection through deep learning reconstruction with IQMR technology.Abdominal radiology (New York) · 2026Article
- Comprehensive comparative analysis of explainable deep learning model for differentiation of brucellar spondylitis and tuberculous spondylitis through MRI sequences.European journal of medical research · 2025Article
- Layer by Layer: Assessing AI Diagnostic Accuracy With Incremental Case Information in Neuroradiology.Cureus · 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
- MRI-Based Head and Neck Tumor Segmentation Using nnU-Net with 15-Fold Cross-Validation Ensemble.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
- Applications and challenges of neural networks in otolaryngology (Review).Biomedical reports · 2024Review
- Virtual tumor mapping and margin control with 3-D planning and navigation.Innovative surgical sciences · 2024Review
- Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning.European radiology · 2023Article
- Auto-detection and segmentation of involved lymph nodes in HPV-associated oropharyngeal cancer using a convolutional deep learning neural network.Clinical and translational radiation oncology · 2022Article
- Deep learning-based auto segmentation using generative adversarial network on magnetic resonance images obtained for head and neck cancer patients.Journal of applied clinical medical physics · 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
- 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
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9 authors.
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Abstract
backgroundAutomatic tumor segmentation based on Convolutional Neural Networks (CNNs) has shown to be a valuable tool in treatment planning and clinical decision making. We investigate the influence of 7 MRI input channels of a CNN with respect to the segmentation performance of head&neck cancer.
methodsHead&neck cancer patients underwent multi-parametric MRI including T2w, pre- and post-contrast T1w, T2*, perfusion (k
resultsThe CNN segmentation performance scored up to a Dice similarity coefficient (DSC) of 0.65. The network trained without T2* data generally yielded the worst results, with ΔDSC
conclusionsWe developed a method to reduce overall scan times in MRI protocols by prioritizing those sequences that add most unique information for the task of automatic tumor segmentation. The optimized CNNs could be used to aid in the definition of the GTVs in radiotherapy planning, and the faster imaging protocols will reduce patient scan times which can increase patient compliance.
trial registrationThe trial was registered retrospectively at the German Register for Clinical Studies (DRKS) under register number DRKS00003830 on August 20th, 2015.
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