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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:

Fully convolutional networks for automated segmentation of abdominal adipose tissue depots in multicenter water–fat MRI
Taro Langner, Anders Hedström, Katharina Mörwald, et al.
Magnetic Resonance in Medicine (2018) Vol. 81, Iss. 4, pp. 2736-2745
Open Access | Times Cited: 50

Showing 1-25 of 50 citing articles:

FatSegNet: A fully automated deep learning pipeline for adipose tissue segmentation on abdominal dixon MRI
Santiago Estrada, Ran Lu, Sailesh Conjeti, et al.
Magnetic Resonance in Medicine (2019) Vol. 83, Iss. 4, pp. 1471-1483
Open Access | Times Cited: 84
Left ventricle automatic segmentation in cardiac MRI using a combined CNN and U-net approach
Bofeng Wu, Ying Fang, Xiaobo Lai
Computerized Medical Imaging and Graphics (2020) Vol. 82, pp. 101719-101719
Closed Access | Times Cited: 61
Segmenting Purple Rapeseed Leaves in the Field from UAV RGB Imagery Using Deep Learning as an Auxiliary Means for Nitrogen Stress Detection
Jian Zhang, Tianjin Xie, Chenghai Yang, et al.
Remote Sensing (2020) Vol. 12, Iss. 9, pp. 1403-1403
Open Access | Times Cited: 48
Fully Automated and Standardized Segmentation of Adipose Tissue Compartments via Deep Learning in 3D Whole-Body MRI of Epidemiologic Cohort Studies
Thomas Küstner, Tobias Hepp, Marc Fischer, et al.
Radiology Artificial Intelligence (2020) Vol. 2, Iss. 6, pp. e200010-e200010
Open Access | Times Cited: 41
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: 38
A review of the application of deep learning in obesity: From early prediction aid to advanced management assistance
X. Yi, Yangzhige He, Shan Gao, et al.
Diabetes & Metabolic Syndrome Clinical Research & Reviews (2024) Vol. 18, Iss. 4, pp. 103000-103000
Closed Access | Times Cited: 6
An Effective CNN Method for Fully Automated Segmenting Subcutaneous and Visceral Adipose Tissue on CT Scans
Zheng Wang, Yu Meng, Futian Weng, et al.
Annals of Biomedical Engineering (2019) Vol. 48, Iss. 1, pp. 312-328
Closed Access | Times Cited: 39
Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs
Sevgi Gökçe Kafalı, Shu‐Fu Shih, Xin-Zhou Li, et al.
Magnetic Resonance Materials in Physics Biology and Medicine (2024) Vol. 37, Iss. 3, pp. 491-506
Open Access | Times Cited: 4
Generative Adversarial Network and Diffusion Model for Unsupervised Venous Malformation Segmentation on Mice Whole Body MRI
Antoine Fraissenon, Alisa Kugusheva, Sophia Ladraa, et al.
(2025)
Closed Access
Magnetic Resonance Imaging and Bioelectrical Impedance Analysis to Assess Visceral and Abdominal Adipose Tissue
Oliver Chaudry, Alexandra Grimm, Andreas Friedberger, et al.
Obesity (2020) Vol. 28, Iss. 2, pp. 277-283
Open Access | Times Cited: 25
Large-scale biometry with interpretable neural network regression on UK Biobank body MRI
Taro Langner, Robin Strand, Håkan Åhlström, et al.
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 25
Deep learning‐based automatic pipeline for quantitative assessment of thigh muscle morphology and fatty infiltration
Sibaji Gaj, Brendan Eck, Dongxing Xie, et al.
Magnetic Resonance in Medicine (2023) Vol. 89, Iss. 6, pp. 2441-2455
Open Access | Times Cited: 9
Separation of water and fat signal in whole‐body gradient echo scans using convolutional neural networks
Jonathan Andersson, Håkan Åhlström, Joel Kullberg
Magnetic Resonance in Medicine (2019) Vol. 82, Iss. 3, pp. 1177-1186
Open Access | Times Cited: 18
Current and emerging artificial intelligence applications for pediatric abdominal imaging
Jonathan R. Dillman, Elan Somasundaram, Samuel L. Brady, et al.
Pediatric Radiology (2021) Vol. 52, Iss. 11, pp. 2139-2148
Closed Access | Times Cited: 15
A systematic review on application of deep learning in digestive system image processing
Huangming Zhuang, Jixiang Zhang, Fei Liao
The Visual Computer (2021) Vol. 39, Iss. 6, pp. 2207-2222
Open Access | Times Cited: 15
Analysis of volume and topography of adipose tissue in the trunk: Results of MRI of 11,141 participants in the German National Cohort
Tobias Haueise, Fritz Schick, Norbert Stefan, et al.
Science Advances (2023) Vol. 9, Iss. 19
Open Access | Times Cited: 6
Automated Deep Learning–Based Segmentation of Abdominal Adipose Tissue on Dixon MRI in Adolescents: A Prospective Population-Based Study
Tong Wu, Santiago Estrada, Renza van Gils, et al.
American Journal of Roentgenology (2023) Vol. 222, Iss. 1
Open Access | Times Cited: 6
FM-Net: A Fully Automatic Deep Learning Pipeline for Epicardial Adipose Tissue Segmentation
Fan Feng, Carl‐Johan Carlhäll, Yongyao Tan, et al.
Lecture notes in computer science (2024), pp. 88-97
Closed Access | Times Cited: 2
Adipose Tissue Segmentation in Unlabeled Abdomen MRI using Cross Modality Domain Adaptation
Samira Masoudi, Syed Muhammad Anwar, Stephanie A. Harmon, et al.
(2020), pp. 1624-1628
Open Access | Times Cited: 13
Automated Segmentation of Visceral, Deep Subcutaneous, and Superficial Subcutaneous Adipose Tissue Volumes in MRI of Neonates and Young Children
Yeshe Kway, Kashthuri Thirumurugan, Mya Thway Tint, et al.
Radiology Artificial Intelligence (2021) Vol. 3, Iss. 5, pp. e200304-e200304
Open Access | Times Cited: 12
Automatic segmentation of whole-body adipose tissue from magnetic resonance fat fraction images based on machine learning
Zhiming Wang, Chuanli Cheng, Hao Peng, et al.
Magnetic Resonance Materials in Physics Biology and Medicine (2021) Vol. 35, Iss. 2, pp. 193-203
Closed Access | Times Cited: 10
Uncertainty-aware body composition analysis with deep regression ensembles on UK Biobank MRI
Taro Langner, Fredrik Gustafsson, Benny Avelin, et al.
Computerized Medical Imaging and Graphics (2021) Vol. 93, pp. 101994-101994
Open Access | Times Cited: 10
The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic Review
Zsombor Zrubka, Gábor Kertész, László Gulàcsi, et al.
Journal of Medical Internet Research (2023) Vol. 26, pp. e47430-e47430
Open Access | Times Cited: 4
AATCT-IDS: A benchmark Abdominal Adipose Tissue CT Image Dataset for image denoising, semantic segmentation, and radiomics evaluation
Zhiyu Ma, Chen Li, Tianming Du, et al.
Computers in Biology and Medicine (2024) Vol. 177, pp. 108628-108628
Open Access | Times Cited: 1
A Combined Region- and Pixel-Based Deep Learning Approach for Quantifying Abdominal Adipose Tissue in Adolescents Using Dixon Magnetic Resonance Imaging
Olanrewaju A. Ogunleye, Harish RaviPrakash, Ashlee M. Simmons, et al.
Tomography (2023) Vol. 9, Iss. 1, pp. 139-149
Open Access | Times Cited: 3
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