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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Automated Tumor Segmentation in Radiotherapy
Ricky R. Savjani, Michael Lauria, Supratik Bose, et al.
Seminars in Radiation Oncology (2022) Vol. 32, Iss. 4, pp. 319-329
Open Access | Times Cited: 27

Showing 1-25 of 27 citing articles:

Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT
Vincent Andrearczyk, Valentin Oreiller, Moamen Abobakr, et al.
Lecture notes in computer science (2023), pp. 1-30
Closed Access | Times Cited: 42
Evaluation of Deep Learning Clinical Target Volumes Auto-Contouring for Magnetic Resonance Imaging-Guided Online Adaptive Treatment of Rectal Cancer
N Silverio, Wouter van den Wollenberg, Anja Betgen, et al.
Advances in Radiation Oncology (2024) Vol. 9, Iss. 6, pp. 101483-101483
Open Access | Times Cited: 7
An active learning approach to train a deep learning algorithm for tumor segmentation from brain MR images
Andrew S. Boehringer, Amirhossein Sanaat, Hossein Arabi, et al.
Insights into Imaging (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 14
A network score-based metric to optimize the quality assurance of automatic radiotherapy target segmentations
Roque Rodríguez Outeiral, N Silverio, P. González, et al.
Physics and Imaging in Radiation Oncology (2023) Vol. 28, pp. 100500-100500
Open Access | Times Cited: 12
Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning
Jaakko Sahlsten, Joel Jaskari, Kareem A. Wahid, et al.
Communications Medicine (2024) Vol. 4, Iss. 1
Open Access | Times Cited: 4
Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using 18F-FDG PET/CT Imaging: Contribution of Artificial Intelligence and Radiomics Analysis
Saeed Shafiee Hasanabadi, Seyed Mahmud Reza Aghamiri, Ahmad Ali Abin, et al.
Cancers (2024) Vol. 16, Iss. 20, pp. 3511-3511
Open Access | Times Cited: 4
Practical and technical key challenges in head and neck adaptive radiotherapy: The GORTEC point of view
N. Delaby, A. Barateau, Sophie Chiavassa, et al.
Physica Medica (2023) Vol. 109, pp. 102568-102568
Open Access | Times Cited: 10
The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors
Yixin Wang, Zongtao Hu, Hongzhi Wang
Insights into Imaging (2025) Vol. 16, Iss. 1
Open Access
Smart contours: deep learning-driven internal gross tumor volume delineation in non-small cell lung cancer using 4D CT maximum and average intensity projections
Yuling Huang, Mingming Luo, Zan Luo, et al.
Radiation Oncology (2025) Vol. 20, Iss. 1
Open Access
Integrating AI Into PET/CT and PET/MRI
R. T. Subhalakshmi, M. Nivaashini, Gowrishankar Ganesh, et al.
Advances in computational intelligence and robotics book series (2025), pp. 311-332
Closed Access
Deep learning for malignant lymph node segmentation and detection: a review
Wenxia Wu, Adrien Laville, Eric Deutsch, et al.
Frontiers in Immunology (2025) Vol. 16
Open Access
Enhancing non-small cell lung cancer radiotherapy planning: A deep learning-based multi-modal fusion approach for accurate GTV segmentation
Shaik Ummay Atiya, N. V. K. Ramesh
Biomedical Signal Processing and Control (2024) Vol. 92, pp. 105987-105987
Closed Access | Times Cited: 3
Motion compensated cone-beam CT reconstruction using an a priori motion model from CT simulation: a pilot study
Michael Lauria, Claudia Miller, Kamal Singhrao, et al.
Physics in Medicine and Biology (2024) Vol. 69, Iss. 7, pp. 075022-075022
Closed Access | Times Cited: 3
Application of simultaneous uncertainty quantification for image segmentation with probabilistic deep learning: Performance benchmarking of oropharyngeal cancer target delineation as a use-case
Jaakko Sahlsten, Joel Jaskari, Kareem A. Wahid, et al.
medRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 7
Deep CNN-based Fully Automated Segmentation of Pelvic Multi-Organ on CT Images for Prostate Cancer Radiotherapy
Bahram Mofid, Sayed Mohammad Modarres Mosalla, Masumeh Goodarzi, et al.
Journal of Biomedical Physics and Engineering (2024) Vol. online
Open Access | Times Cited: 2
Geometric and dosimetric evaluation for breast and regional nodal auto‐segmentation structures
Tiffany Tsui, Alexander R. Podgorsak, John C. Roeske, et al.
Journal of Applied Clinical Medical Physics (2024) Vol. 25, Iss. 10
Open Access | Times Cited: 2
Clinical adoption of deep learning target auto-segmentation for radiation therapy: challenges, clinical risks, and mitigation strategies
Alessia de Biase, Nanna M. Sijtsema, Tomas Janssen, et al.
Deleted Journal (2024) Vol. 1, Iss. 1
Open Access | Times Cited: 2
Potential of Deep Learning in Quantitative Magnetic Resonance Imaging for Personalized Radiotherapy
Oliver J. Gurney‐Champion, Guillaume Landry, Kathrine Røe Redalen, et al.
Seminars in Radiation Oncology (2022) Vol. 32, Iss. 4, pp. 377-388
Closed Access | Times Cited: 10
Stress-testing pelvic autosegmentation algorithms using anatomical edge cases
Aasheesh Kanwar, B. Merz, C. Claunch, et al.
Physics and Imaging in Radiation Oncology (2023) Vol. 25, pp. 100413-100413
Open Access | Times Cited: 5
Resection cavity auto‐contouring for patients with pediatric medulloblastoma using only CT information
Soleil Hernandez, Callistus Nguyen, Skylar Gay, et al.
Journal of Applied Clinical Medical Physics (2023) Vol. 24, Iss. 7
Open Access | Times Cited: 4
AI-based auto-segmentation: advantages in delineation, absorbed dose-distribution and logistics
Gustavo R. Sarria, Fabian Kugel, F. Roehner, et al.
Advances in Radiation Oncology (2023) Vol. 9, Iss. 3, pp. 101394-101394
Open Access | Times Cited: 4
Benchmarking Scalable Epistemic Uncertainty Quantification in Organ Segmentation
Jadie Adams, Shireen Elhabian
Lecture notes in computer science (2023), pp. 53-63
Closed Access | Times Cited: 3
RFLSE: Joint radiomics feature‐enhanced level‐set segmentation for low‐contrast SPECT/CT tumour images
Zhaotong Guo, Pinle Qin, Jianchao Zeng, et al.
IET Image Processing (2024) Vol. 18, Iss. 10, pp. 2715-2731
Open Access
HU Coefficient: A Clinically Oriented Metric to Evaluate Contour Accuracy in Radiation Therapy
Harold Yang Hu, Shaw Yang Hu, Min Yang, et al.
Research Square (Research Square) (2024)
Closed Access
Perspectives for using artificial intelligence techniques in radiation therapy
Guillaume Landry, Christopher Kurz, Adrian Thummerer
The European Physical Journal Plus (2024) Vol. 139, Iss. 10
Closed Access
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