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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:

Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment
Robert Hemke, Colleen Buckless, Andrew Tsao, et al.
Skeletal Radiology (2019) Vol. 49, Iss. 3, pp. 387-395
Open Access | Times Cited: 75

Showing 1-25 of 75 citing articles:

A review of medical image data augmentation techniques for deep learning applications
Phillip Chlap, Hang Min, Nym Vandenberg, et al.
Journal of Medical Imaging and Radiation Oncology (2021) Vol. 65, Iss. 5, pp. 545-563
Closed Access | Times Cited: 621
Population-Scale CT-based Body Composition Analysis of a Large Outpatient Population Using Deep Learning to Derive Age-, Sex-, and Race-specific Reference Curves
Kirti Magudia, Christopher P. Bridge, Camden P. Bay, et al.
Radiology (2020) Vol. 298, Iss. 2, pp. 319-329
Open Access | Times Cited: 110
Value-Added Opportunistic CT: Insights Into Osteoporosis and Sarcopenia
Robert D. Boutin, Leon Lenchik
American Journal of Roentgenology (2020) Vol. 215, Iss. 3, pp. 582-594
Closed Access | Times Cited: 100
Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: A review
Antti Tolonen, Tomppa Pakarinen, Antti Sassi, et al.
European Journal of Radiology (2021) Vol. 145, pp. 109943-109943
Open Access | Times Cited: 92
Deep learning to segment pelvic bones: large-scale CT datasets and baseline models
Pengbo Liu, Hu Han, Yuanqi Du, et al.
International Journal of Computer Assisted Radiology and Surgery (2021) Vol. 16, Iss. 5, pp. 749-756
Closed Access | Times Cited: 82
Machine learning methods to support personalized neuromusculoskeletal modelling
David J. Saxby, Bryce A. Killen, Claudio Pizzolato, et al.
Biomechanics and Modeling in Mechanobiology (2020) Vol. 19, Iss. 4, pp. 1169-1185
Open Access | Times Cited: 73
Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions
Muhammad Waqas Nadeem, Hock Guan Goh, Ali Abid, et al.
Diagnostics (2020) Vol. 10, Iss. 10, pp. 781-781
Open Access | Times Cited: 70
Segmentation of the iliac crest from CT-data for virtual surgical planning of facial reconstruction surgery using deep learning
Stefan Raith, Tobias Pankert, Jacinto C. Nascimento, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Artificial intelligence in orthopedics: three strategies for deep learning with orthopedic specific imaging
Sunho Ko, Ayoosh Pareek, Du Hyun Ro, et al.
Knee Surgery Sports Traumatology Arthroscopy (2022) Vol. 30, Iss. 3, pp. 758-761
Open Access | Times Cited: 34
Artificial intelligence for body composition and sarcopenia evaluation on computed tomography: A systematic review and meta-analysis
Sergei Bedrikovetski, Warren Seow, Hidde M. Kroon, et al.
European Journal of Radiology (2022) Vol. 149, pp. 110218-110218
Closed Access | Times Cited: 34
Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies
Lorenza Bonaldi, Andrea Pretto, Carmelo Pirri, et al.
Bioengineering (2023) Vol. 10, Iss. 2, pp. 137-137
Open Access | Times Cited: 16
A deep learning model based on the attention mechanism for automatic segmentation of abdominal muscle and fat for body composition assessment
Hao Shen, He Pin, Ren Ya, et al.
Quantitative Imaging in Medicine and Surgery (2023) Vol. 13, Iss. 3, pp. 1384-1398
Open Access | Times Cited: 16
Automated detection and classification of shoulder arthroplasty models using deep learning
Paul H. Yi, Tae Kyung Kim, Jinchi Wei, et al.
Skeletal Radiology (2020) Vol. 49, Iss. 10, pp. 1623-1632
Closed Access | Times Cited: 47
Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets
Elham Taghizadeh, Oskar Truffer, Fabio Becce, et al.
European Radiology (2020) Vol. 31, Iss. 1, pp. 181-190
Open Access | Times Cited: 43
Artificial intelligence and abdominal adipose tissue analysis: a literature review
Federico Greco, Carlo Augusto Mallio
Quantitative Imaging in Medicine and Surgery (2021) Vol. 11, Iss. 10, pp. 4461-4474
Open Access | Times Cited: 37
Body composition predictors of outcome in patients with COVID-19
Katherine M. Bunnell, Tanayott Thaweethai, Colleen Buckless, et al.
International Journal of Obesity (2021) Vol. 45, Iss. 10, pp. 2238-2243
Open Access | Times Cited: 37
Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients
Leanne L. G. C. Ackermans, Leroy Volmer, Leonard Wee, et al.
Sensors (2021) Vol. 21, Iss. 6, pp. 2083-2083
Open Access | Times Cited: 35
The synergy of synchrotron imaging and convolutional neural networks towards the detection of human micro-scale bone architecture and damage
Federica Buccino, Irene Aiazzi, Alessandro Casto, et al.
Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials (2022) Vol. 137, pp. 105576-105576
Closed Access | Times Cited: 23
Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions
Tarig Elhakim, Kelly Trinh, Arian Mansur, et al.
Diagnostics (2023) Vol. 13, Iss. 5, pp. 968-968
Open Access | Times Cited: 15
Automatic segmentation of large-scale CT image datasets for detailed body composition analysis
Nouman Ahmad, Robin Strand, Björn Sparresäter, et al.
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 14
Automated Segmentation of Trunk Musculature with a Deep CNN Trained from Sparse Annotations in Radiation Therapy Patients with Metastatic Spine Disease.
Valéria Costa‐Hong, Steve Pieper, Joanna N. James, et al.
medRxiv (Cold Spring Harbor Laboratory) (2025)
Open Access
Deep Learning Technique for Automatic Segmentation of Proximal Hip Musculoskeletal Tissues From CT Scan Images: A MrOS Study
Mahdi Imani, Jared Buratto, Thâng Dao, et al.
Journal of Cachexia Sarcopenia and Muscle (2025) Vol. 16, Iss. 2
Open Access
Artificial Intelligence in the Evaluation of Body Composition
Benjamin Wang, Martin Torriani
Seminars in Musculoskeletal Radiology (2020) Vol. 24, Iss. 01, pp. 030-037
Open Access | Times Cited: 32
Use of artificial intelligence in the imaging of sarcopenia: A narrative review of current status and perspectives
Miłosz Rozynek, Iwona Kucybała, Andrzej Urbanik, et al.
Nutrition (2021) Vol. 89, pp. 111227-111227
Closed Access | Times Cited: 32
A Review of CT-Based Fracture Risk Assessment with Finite Element Modeling and Machine Learning
Ingmar Fleps, Elise F. Morgan
Current Osteoporosis Reports (2022) Vol. 20, Iss. 5, pp. 309-319
Open Access | Times Cited: 20
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