Comparing different CT, PET and MRI multi-modality image combinations for deep learning-based head and neck tumor segmentation-Reference-Cited by-同舟云学术

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Comparing different CT, PET and MRI multi-modality image combinations for deep learning-based head and neck tumor segmentation

Published:2021-07-15 Issue:11 Volume:60 Page:1399-1406
ISSN:0284-186X
Container-title:Acta Oncologica
language:en
Short-container-title:Acta Oncologica

Author:

Ren Jintao¹²³^ORCID,Eriksen Jesper Grau¹⁴^ORCID,Nijkamp Jasper¹²^ORCID,Korreman Stine Sofia¹²³^ORCID

Affiliation:

1. Department of Clinical Medicine, Aarhus University, Aarhus, Denmark

2. Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark

3. Department of Oncology, Aarhus University Hospital, Aarhus, Denmark

4. Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark

Funder

Aarhus University Research Foundation

Danish Cancer Society

DCCC Radiotherapy - The Danish National Research Center for Radiotherapy

Danish Comprehensive Cancer Center

Publisher

Informa UK Limited

Subject

Radiology Nuclear Medicine and imaging,Oncology,Hematology,General Medicine

Link

https://www.tandfonline.com/doi/pdf/10.1080/0284186X.2021.1949034

Reference37 articles.

1. Target volume selection and delineation (T and N) for primary radiation treatment of oral cavity, oropharyngeal, hypopharyngeal and laryngeal squamous cell carcinoma

2. Uncertainties in volume delineation in radiation oncology: A systematic review and recommendations for future studies

3. On the Delineation of the Gross Tumor Volume and Clinical Target Volume for Head and Neck Squamous Cell Carcinomas

4. Delineation of the primary tumour Clinical Target Volumes (CTV-P) in laryngeal, hypopharyngeal, oropharyngeal and oral cavity squamous cell carcinoma: AIRO, CACA, DAHANCA, EORTC, GEORCC, GORTEC, HKNPCSG, HNCIG, IAG-KHT, LPRHHT, NCIC CTG, NCRI, NRG Oncology, PHNS, SBRT, SOMERA, SRO, SSHNO, TROG consensus guidelines

5. Volumetric and dosimetric comparison of two delineation guidelines for the radiation treatment of laryngeal squamous cell carcinoma

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1. Automatic delineation and prognostic assessment of head and neck tumor lesion in multi-modality positron emission tomography / computed tomography images based on deep learning: A survey;Neurocomputing;2024-12

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3. Deep learning with uncertainty estimation for automatic tumor segmentation in PET/CT of head and neck cancers: impact of model complexity, image processing and augmentation;Biomedical Physics & Engineering Express;2024-08-30

4. One-click annotation to improve segmentation by a convolutional neural network for PET images of head and neck cancer patients;Network Modeling Analysis in Health Informatics and Bioinformatics;2024-08-28

5. Enhancing the reliability of deep learning-based head and neck tumour segmentation using uncertainty estimation with multi-modal images;Physics in Medicine & Biology;2024-08-05

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