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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:

The first MICCAI challenge on PET tumor segmentation
Mathieu Hatt, Baptiste Laurent, Anouar Ouahabi, et al.
Medical Image Analysis (2017) Vol. 44, pp. 177-195
Open Access | Times Cited: 130

Showing 1-25 of 130 citing articles:

Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide
Shelly Soffer, Avi Ben-Cohen, Orit Shimon, et al.
Radiology (2019) Vol. 290, Iss. 3, pp. 590-606
Closed Access | Times Cited: 432
Survey on deep learning for radiotherapy
P. Meyer, Vincent Noblet, C. Mazzara, et al.
Computers in Biology and Medicine (2018) Vol. 98, pp. 126-146
Closed Access | Times Cited: 277
Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis
Alex Zwanenburg
European Journal of Nuclear Medicine and Molecular Imaging (2019) Vol. 46, Iss. 13, pp. 2638-2655
Closed Access | Times Cited: 247
Head and neck tumor segmentation in PET/CT: The HECKTOR challenge
Valentin Oreiller, Vincent Andrearczyk, Mario Jreige, et al.
Medical Image Analysis (2021) Vol. 77, pp. 102336-102336
Open Access | Times Cited: 150
Pretreatment 18F-FDG PET/CT Radiomics Predict Local Recurrence in Patients Treated with Stereotactic Body Radiotherapy for Early-Stage Non–Small Cell Lung Cancer: A Multicentric Study
Gurvan Dissaux, Dimitris Visvikis, Ronrick Da‐ano, et al.
Journal of Nuclear Medicine (2019) Vol. 61, Iss. 6, pp. 814-820
Open Access | Times Cited: 146
Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications
Dimitris Visvikis, Catherine Cheze Le Rest, Vincent Jaouen, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2019) Vol. 46, Iss. 13, pp. 2630-2637
Closed Access | Times Cited: 144
Advanced Monte Carlo simulations of emission tomography imaging systems with GATE
David Sarrut, M. Bała, Manuel Bardiès, et al.
Physics in Medicine and Biology (2021) Vol. 66, Iss. 10, pp. 10TR03-10TR03
Open Access | Times Cited: 143
Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images
Vincent Andrearczyk, Valentin Oreiller, Sarah Boughdad, et al.
Lecture notes in computer science (2022), pp. 1-37
Closed Access | Times Cited: 80
Joint EANM/SNMMI guideline on radiomics in nuclear medicine
Mathieu Hatt, Aron K. Krizsan, Arman Rahmim, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2022) Vol. 50, Iss. 2, pp. 352-375
Open Access | Times Cited: 78
Challenges and limitations in applying radiomics to PET imaging: Possible opportunities and avenues for research
Alessandro Stefano
Computers in Biology and Medicine (2024) Vol. 179, pp. 108827-108827
Open Access | Times Cited: 15
Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET
Inês Domingues, Gisèle Pereira, Pedro Martins, et al.
Artificial Intelligence Review (2019) Vol. 53, Iss. 6, pp. 4093-4160
Closed Access | Times Cited: 136
Challenges in diffusion MRI tractography – Lessons learned from international benchmark competitions
Kurt G. Schilling, Alessandro Daducci, Klaus Maier‐Hein, et al.
Magnetic Resonance Imaging (2018) Vol. 57, pp. 194-209
Open Access | Times Cited: 131
Deep learning for variational multimodality tumor segmentation in PET/CT
Laquan Li, Xiangming Zhao, Wei Lü, et al.
Neurocomputing (2019) Vol. 392, pp. 277-295
Open Access | Times Cited: 127
Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and Fusion Based on Belief Functions
Chunfeng Lian, Su Ruan, Thierry Denœux, et al.
IEEE Transactions on Image Processing (2018) Vol. 28, Iss. 2, pp. 755-766
Open Access | Times Cited: 104
qPSMA: Semiautomatic Software for Whole-Body Tumor Burden Assessment in Prostate Cancer Using 68Ga-PSMA11 PET/CT
Andrei Gafita, Marie Bieth, Martin Krönke, et al.
Journal of Nuclear Medicine (2019) Vol. 60, Iss. 9, pp. 1277-1283
Open Access | Times Cited: 99
Radiomics: Data Are Also Images
Mathieu Hatt, Catherine Cheze Le Rest, Florent Tixier, et al.
Journal of Nuclear Medicine (2019) Vol. 60, Iss. Supplement 2, pp. 38S-44S
Open Access | Times Cited: 98
RETRACTED: Deep learning for real-time semantic segmentation: Application in ultrasound imaging
Abdeldjalil Ouahabi, Abdelmalik Taleb‐Ahmed
Pattern Recognition Letters (2021) Vol. 144, pp. 27-34
Open Access | Times Cited: 91
Deep neural network for automatic characterization of lesions on 68Ga-PSMA-11 PET/CT
Yu Zhao, Andrei Gafita, Bernd Vollnberg, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2019) Vol. 47, Iss. 3, pp. 603-613
Closed Access | Times Cited: 89
Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods
Tonghe Wang, Yang Lei, Yabo Fu, et al.
Physica Medica (2020) Vol. 76, pp. 294-306
Open Access | Times Cited: 82
Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT
Vincent Andrearczyk, Valentin Oreiller, Mario Jreige, et al.
Lecture notes in computer science (2021), pp. 1-21
Closed Access | Times Cited: 81
Automated Segmentation of Baseline Metabolic Total Tumor Burden in Diffuse Large B-Cell Lymphoma: Which Method Is Most Successful? A Study on Behalf of the PETRA Consortium
Sally F. Barrington, Gerben J. C. Zwezerijnen, Henrica C. W. de Vet, et al.
Journal of Nuclear Medicine (2020) Vol. 62, Iss. 3, pp. 332-337
Open Access | Times Cited: 76
Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies
Simon K. B. Spohn, A. Bettermann, Fabian Bamberg, et al.
Theranostics (2021) Vol. 11, Iss. 16, pp. 8027-8042
Open Access | Times Cited: 56
Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation
Dimitris Visvikis, Philippe Lambin, Kim Beuschau Mauridsen, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2022) Vol. 49, Iss. 13, pp. 4452-4463
Open Access | Times Cited: 52
Deep Segmentation Networks for Segmenting Kidneys and Detecting Kidney Stones in Unenhanced Abdominal CT Images
Dan Li, Chuda Xiao, Yang Liu, et al.
Diagnostics (2022) Vol. 12, Iss. 8, pp. 1788-1788
Open Access | Times Cited: 44
Decentralized Distributed Multi-institutional PET Image Segmentation Using a Federated Deep Learning Framework
Isaac Shiri, Alireza Vafaei Sadr, Mehdi Amini, et al.
Clinical Nuclear Medicine (2022) Vol. 47, Iss. 7, pp. 606-617
Open Access | Times Cited: 40
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