Comparative evaluation of a prototype deep learning algorithm for autosegmentation of normal tissues in head and neck radiotherapy
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Comparative evaluation of a prototype deep learning algorithm for autosegmentation of normal tissues in head and neck radiotherapy
Authors
Keywords
-
Journal
RADIOTHERAPY AND ONCOLOGY
Volume 174, Issue -, Pages 52-58
Publisher
Elsevier BV
Online
2022-07-09
DOI
10.1016/j.radonc.2022.06.024
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Clinical implementation of deep learning contour autosegmentation for prostate radiotherapy
- (2021) Elaine Cha et al. RADIOTHERAPY AND ONCOLOGY
- A Preliminary Experience of Implementing Deep-Learning Based Auto-Segmentation in Head and Neck Cancer: A Study on Real-World Clinical Cases
- (2021) Yang Zhong et al. Frontiers in Oncology
- AI in medical physics: guidelines for publication
- (2021) Issam El Naqa et al. MEDICAL PHYSICS
- Interobserver variability in target volume delineation in definitive radiotherapy for thoracic esophageal cancer: a multi-center study from China
- (2021) Xiao Chang et al. Radiation Oncology
- Evaluating Clinical Acceptability of Organs-at-Risk Segmentation in Head & Neck Cancer (HNC) by Open-Source 3D Convolutional Neural Networks (CNNs)
- (2021) J. Marsilla et al. INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
- Dropout vs. batch normalization: an empirical study of their impact to deep learning
- (2020) Christian Garbin et al. MULTIMEDIA TOOLS AND APPLICATIONS
- Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods
- (2020) Tomaž Vrtovec et al. MEDICAL PHYSICS
- Evaluation of deep learning‐based auto‐segmentation algorithms for delineating clinical target volume and organs at risk involving data for 125 cervical cancer patients
- (2020) Zhi Wang et al. Journal of Applied Clinical Medical Physics
- nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
- (2020) Fabian Isensee et al. NATURE METHODS
- Multi-organ segmentation of the head and neck area: an efficient hierarchical neural networks approach
- (2019) Elias Tappeiner et al. International Journal of Computer Assisted Radiology and Surgery
- Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring
- (2019) Lisanne V. van Dijk et al. RADIOTHERAPY AND ONCOLOGY
- Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning
- (2019) Jordan Wong et al. RADIOTHERAPY AND ONCOLOGY
- American Association of Physicists in Medicine Task Group 263: Standardizing Nomenclatures in Radiation Oncology
- (2018) Charles S. Mayo et al. INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
- Performance of commercially available deformable image registration platforms for contour propagation using patient-based computational phantoms: A multi-institutional study
- (2018) Gianfranco Loi et al. MEDICAL PHYSICS
- Interobserver variations in the delineation of target volumes and organs at risk and their impact on dose distribution in intensity-modulated radiation therapy for nasopharyngeal carcinoma
- (2018) Ying-lin Peng et al. ORAL ONCOLOGY
- Use of auto-segmentation in the delineation of target volumes and organs at risk in head and neck
- (2016) Jia Yi Lim et al. ACTA ONCOLOGICA
- Interactive contour delineation of organs at risk in radiotherapy: Clinical evaluation on NSCLC patients
- (2016) J. Dolz et al. MEDICAL PHYSICS
- Uncertainties in volume delineation in radiation oncology: A systematic review and recommendations for future studies
- (2016) Shalini K. Vinod et al. RADIOTHERAPY AND ONCOLOGY
- A multi-institution pooled analysis of gastrostomy tube dependence in patients with oropharyngeal cancer treated with definitive intensity-modulated radiotherapy
- (2014) Jeremy Setton et al. CANCER
- Evaluation of an automatic segmentation algorithm for definition of head and neck organs at risk
- (2014) David Thomson et al. Radiation Oncology
- Prospective randomized double-blind study of atlas-based organ-at-risk autosegmentation-assisted radiation planning in head and neck cancer
- (2014) Gary V. Walker et al. RADIOTHERAPY AND ONCOLOGY
- Correlation of contouring variation with modeled outcome for conformal non-small cell lung cancer radiotherapy
- (2014) Michael G. Jameson et al. RADIOTHERAPY AND ONCOLOGY
- A System for Continual Quality Improvement of Normal Tissue Delineation for Radiation Therapy Treatment Planning
- (2012) Jennifer Breunig et al. INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
- Categorizing segmentation quality using a quantitative quality assurance algorithm
- (2012) George Rodrigues et al. Journal of Medical Imaging and Radiation Oncology
- 3D Variation in delineation of head and neck organs at risk
- (2012) Charlotte L Brouwer et al. Radiation Oncology
- Clinical Validation of Atlas-Based Auto-Segmentation of Multiple Target Volumes and Normal Tissue (Swallowing/Mastication) Structures in the Head and Neck
- (2010) David N. Teguh et al. INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
- Variations in the Contouring of Organs at Risk: Test Case From a Patient With Oropharyngeal Cancer
- (2010) Benjamin E. Nelms et al. INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
- A review of methods of analysis in contouring studies for radiation oncology
- (2010) Michael G Jameson et al. Journal of Medical Imaging and Radiation Oncology
- Variations in Target Volume Definition for Postoperative Radiotherapy in Stage III Non–Small-Cell Lung Cancer: Analysis of an International Contouring Study
- (2009) Femke O.B. Spoelstra et al. INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreAdd your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload Now