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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:

Dynamic Regulation of Level Set Parameters Using 3D Convolutional Neural Network for Liver Tumor Segmentation
Zhuofu Deng, Qingzhe Guo, Zhiliang Zhu
Journal of Healthcare Engineering (2019) Vol. 2019, pp. 1-17
Open Access | Times Cited: 25

Showing 25 citing articles:

Anatomy-aided deep learning for medical image segmentation: a review
Lu Liu, Jelmer M. Wolterink, Christoph Brüne, et al.
Physics in Medicine and Biology (2021) Vol. 66, Iss. 11, pp. 11TR01-11TR01
Open Access | Times Cited: 64
Deep Learning With 3D Convolutional Neural Network for Noninvasive Prediction of Microvascular Invasion in Hepatocellular Carcinoma
Yongxin Zhang, Xiaofei Lv, Jiliang Qiu, et al.
Journal of Magnetic Resonance Imaging (2021) Vol. 54, Iss. 1, pp. 134-143
Closed Access | Times Cited: 59
A Multiple Layer U-Net, Un-Net, for Liver and Liver Tumor Segmentation in CT
Song-Toan Tran, Ching-Hwa Cheng, Don‐Gey Liu
IEEE Access (2020) Vol. 9, pp. 3752-3764
Open Access | Times Cited: 69
Deep Learning Initialized and Gradient Enhanced Level-Set Based Segmentation for Liver Tumor From CT Images
Yue Zhang, Benxiang Jiang, Jiong Wu, et al.
IEEE Access (2020) Vol. 8, pp. 76056-76068
Open Access | Times Cited: 40
Deep learning in hepatocellular carcinoma: Current status and future perspectives
Joseph Ahn, Touseef Ahmad Qureshi, Amit G. Singal, et al.
World Journal of Hepatology (2021) Vol. 13, Iss. 12, pp. 2039-2051
Open Access | Times Cited: 34
Bridging the Gap Between 2D and 3D Contexts in CT Volume for Liver and Tumor Segmentation
Lei Song, Haoqian Wang, Z. Jane Wang
IEEE Journal of Biomedical and Health Informatics (2021) Vol. 25, Iss. 9, pp. 3450-3459
Closed Access | Times Cited: 33
A diagnosis system by U-net and deep neural network enabled with optimal feature selection for liver tumor detection using CT images
Munipraveena Rela, Nagaraja Rao Suryakari, Ramana Reddy Patil
Multimedia Tools and Applications (2022) Vol. 82, Iss. 3, pp. 3185-3227
Closed Access | Times Cited: 24
An automatic method for segmentation of liver lesions in computed tomography images using deep neural networks
José Denes Lima Araújo, Luana Batista da Cruz, Jonnison Lima Ferreira, et al.
Expert Systems with Applications (2021) Vol. 180, pp. 115064-115064
Closed Access | Times Cited: 30
Multi-stage fuzzy swarm intelligence for automatic hepatic lesion segmentation from CT scans
Ahmed M. Anter, Siddhartha Bhattacharyya, Zhiguo Zhang
Applied Soft Computing (2020) Vol. 96, pp. 106677-106677
Closed Access | Times Cited: 29
Automatic Liver Segmentation in CT Images with Enhanced GAN and Mask Region-Based CNN Architectures
Xiaoqin Wei, Xiaowen Chen, Ce Lai, et al.
BioMed Research International (2021) Vol. 2021, pp. 1-11
Open Access | Times Cited: 26
Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review
Keyur Radiya, Henrik Lykke Joakimsen, Karl Øyvind Mikalsen, et al.
European Radiology (2023) Vol. 33, Iss. 10, pp. 6689-6717
Open Access | Times Cited: 10
Efficient Liver Segmentation using Advanced 3D-DCNN Algorithm on CT Images
S. Subha, U. Kumaran
Engineering Technology & Applied Science Research (2025) Vol. 15, Iss. 1, pp. 19324-19330
Open Access
Multi-Scale Convolutional Attention and Structural Re-Parameterized Residual-Based 3D U-Net for Liver and Liver Tumor Segmentation from CT
Ziwei Song, Weiwei Wu, Shuicai Wu
Sensors (2025) Vol. 25, Iss. 6, pp. 1814-1814
Open Access
Study on strategy of CT image sequence segmentation for liver and tumor based on U-Net and Bi-ConvLSTM
Jing Li, Xue Ou, Nanyan Shen, et al.
Expert Systems with Applications (2021) Vol. 180, pp. 115008-115008
Closed Access | Times Cited: 21
Pyramid graph cut: Integrating intensity and gradient information for grayscale medical image segmentation
Thanongchai Siriapisith, Worapan Kusakunniran, Peter Haddawy
Computers in Biology and Medicine (2020) Vol. 126, pp. 103997-103997
Closed Access | Times Cited: 23
Three-Dimensional Liver Image Segmentation Using Generative Adversarial Networks Based on Feature Restoration
Runnan He, Shiqi Xu, Yashu Liu, et al.
Frontiers in Medicine (2022) Vol. 8
Open Access | Times Cited: 14
Radiomics for liver tumours
Constantin Dreher, Philipp Linde, Judit Boda‐Heggemann, et al.
Strahlentherapie und Onkologie (2020) Vol. 196, Iss. 10, pp. 888-899
Open Access | Times Cited: 20
Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network
Shuxin Wang, Shilei Cao, Zhizhong Chai, et al.
IEEE Transactions on Medical Imaging (2020) Vol. 39, Iss. 12, pp. 4174-4185
Open Access | Times Cited: 19
PSO-PSP-Net + InceptionV3: An optimized hyper-parameter tuned Computer-Aided Diagnostic model for liver tumor detection using CT scan slices
Jaspreet Kaur, Prabhpreet Kaur
Biomedical Signal Processing and Control (2024) Vol. 95, pp. 106442-106442
Closed Access | Times Cited: 2
Automatic segmentation of kidney and liver tumors in CT images
Dina B. Efremova, Д. А. Коновалов, Thanongchai Siriapisith, et al.
(2019)
Open Access | Times Cited: 17
Clinical application of mask region-based convolutional neural network for the automatic detection and segmentation of abnormal liver density based on hepatocellular carcinoma computed tomography datasets
Ching-Juei Yang, Chien‐Kuo Wang, Yu-Hua Fang, et al.
PLoS ONE (2021) Vol. 16, Iss. 8, pp. e0255605-e0255605
Open Access | Times Cited: 13
Adaptive Localizing Region-Based Level Set for Segmentation of Maxillary Sinus Based on Convolutional Neural Networks
Xianglong Qi, Jie Zhong, Shengjia Cui
Computational Intelligence and Neuroscience (2021) Vol. 2021, Iss. 1
Open Access | Times Cited: 3
Integrated Autoencoder-Level Set Method Outperforms Autoencoder for Novelty Detection
Shuo Liu Shuo Liu, Xuemei Ding, Damien Coyle
2022 International Joint Conference on Neural Networks (IJCNN) (2022), pp. 1-8
Open Access | Times Cited: 2
Automatic segmentation of kidney and liver tumors in CT images.
Dina B. Efremova, Dmitry A. Konovalov, Thanongchai Siriapisith, et al.
arXiv (Cornell University) (2019)
Closed Access | Times Cited: 1
[Automatic three-dimensional segmentation of liver and tumors regions based on conditional generative adversarial networks].
Zelin Zhang, Baoming Li, Jun Xu
PubMed (2021) Vol. 38, Iss. 1, pp. 80-88
Closed Access | Times Cited: 1
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