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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:

Improved segmentation of low-contrast lesions using sigmoid edge model
Amir Hossein Foruzan, Yen‐Wei Chen
International Journal of Computer Assisted Radiology and Surgery (2015) Vol. 11, Iss. 7, pp. 1267-1283
Closed Access | Times Cited: 60

Showing 1-25 of 60 citing articles:

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
Xiaomeng Li, Hao Chen, Xiaojuan Qi, et al.
IEEE Transactions on Medical Imaging (2018) Vol. 37, Iss. 12, pp. 2663-2674
Open Access | Times Cited: 2026
Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
Patrick Ferdinand Christ, Mohamed Ezzeldin A. Elshaer, Florian Ettlinger, et al.
Lecture notes in computer science (2016), pp. 415-423
Closed Access | Times Cited: 617
Practical utility of liver segmentation methods in clinical surgeries and interventions
Mohammed Yusuf Ansari, Alhusain Abdalla, Mohammed Yaqoob Ansari, et al.
BMC Medical Imaging (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 72
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks.
Patrick Ferdinand Christ, Florian Ettlinger, Felix Grün, et al.
arXiv (Cornell University) (2017)
Closed Access | Times Cited: 149
Channel-Unet: A Spatial Channel-Wise Convolutional Neural Network for Liver and Tumors Segmentation
Yilong Chen, Kai Wang, Xiangyun Liao, et al.
Frontiers in Genetics (2019) Vol. 10
Open Access | Times Cited: 133
Light-Weight Hybrid Convolutional Network for Liver Tumor Segmentation
Jianpeng Zhang, Yutong Xie, Pingping Zhang, et al.
(2019), pp. 4271-4277
Open Access | Times Cited: 103
Efficient 3D Deep Learning Model for Medical Image Semantic Segmentation
Nasser Alalwan, Amr Abozeid, AbdAllah A. ElHabshy, et al.
Alexandria Engineering Journal (2020) Vol. 60, Iss. 1, pp. 1231-1239
Open Access | Times Cited: 100
Multi-Receptive-Field CNN for Semantic Segmentation of Medical Images
Liangliang Liu, Fang‐Xiang Wu, Yu‐Ping Wang, et al.
IEEE Journal of Biomedical and Health Informatics (2020) Vol. 24, Iss. 11, pp. 3215-3225
Closed Access | Times Cited: 89
Dynamic adaptive residual network for liver CT image segmentation
Xiwang Xie, Weidong Zhang, Huadeng Wang, et al.
Computers & Electrical Engineering (2021) Vol. 91, pp. 107024-107024
Closed Access | Times Cited: 73
RMS-UNet: Residual multi-scale UNet for liver and lesion segmentation
Rayyan Azam Khan, Yigang Luo, Fang‐Xiang Wu
Artificial Intelligence in Medicine (2022) Vol. 124, pp. 102231-102231
Closed Access | Times Cited: 53
Adaptive local window for level set segmentation of CT and MRI liver lesions
Assaf Hoogi, Christopher F. Beaulieu, Guilherme Moura Cunha, et al.
Medical Image Analysis (2017) Vol. 37, pp. 46-55
Open Access | Times Cited: 70
PA‐ResSeg: A phase attention residual network for liver tumor segmentation from multiphase CT images
Yingying Xu, Ming Cai, Lanfen Lin, et al.
Medical Physics (2021) Vol. 48, Iss. 7, pp. 3752-3766
Open Access | Times Cited: 49
3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C-Means and Graph Cuts
Weiwei Wu, Shuicai Wu, Zhuhuang Zhou, et al.
BioMed Research International (2017) Vol. 2017, pp. 1-11
Open Access | Times Cited: 60
E$$^2$$Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans
Youbao Tang, Yuxing Tang, Yingying Zhu, et al.
Lecture notes in computer science (2020), pp. 512-522
Closed Access | Times Cited: 49
GA-UNet: UNet-based framework for segmentation of 2D and 3D medical images applicable on heterogeneous datasets
Amrita Kaur, Lakhwinder Kaur, Ashima Singh
Neural Computing and Applications (2021) Vol. 33, Iss. 21, pp. 14991-15025
Closed Access | Times Cited: 34
Semi-automatic liver tumor segmentation with adaptive region growing and graph cuts
Zhen Yang, Yuqian Zhao, Miao Liao, et al.
Biomedical Signal Processing and Control (2021) Vol. 68, pp. 102670-102670
Closed Access | Times Cited: 29
Detection of Liver Tumor Candidates from CT Images Using Deep Convolutional Neural Networks
Yoshihiro Todoroki, Xian‐Hua Han, Yutaro Iwamoto, et al.
Smart innovation, systems and technologies (2017), pp. 140-145
Closed Access | Times Cited: 35
A generalized active shape model for segmentation of liver in low-contrast CT volumes
Mina Esfandiarkhani, Amir Hossein Foruzan
Computers in Biology and Medicine (2017) Vol. 82, pp. 59-70
Closed Access | Times Cited: 31
GAN-Driven Liver Tumor Segmentation: Enhancing Accuracy in Biomedical Imaging
Ankur Biswas, Santi P. Maity, Rita Banik, et al.
SN Computer Science (2024) Vol. 5, Iss. 5
Closed Access | Times Cited: 3
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
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
LLRHNet: Multiple Lesions Segmentation Using Local-Long Range Features
Liangliang Liu, Ying Wang, Chang Jing, et al.
Frontiers in Neuroinformatics (2022) Vol. 16
Open Access | Times Cited: 13
Deep Learning Combined with Radiologist’s Intervention Achieves Accurate Segmentation of Hepatocellular Carcinoma in Dual-Phase Magnetic Resonance Images
Yufeng Ye, Naiwen Zhang, Dasheng Wu, et al.
BioMed Research International (2024) Vol. 2024, pp. 1-13
Open Access | Times Cited: 2
A Unified Level Set Framework Combining Hybrid Algorithms for Liver and Liver Tumor Segmentation in CT Images
Zhou Zheng, Xuechang Zhang, H. M. Xu, et al.
BioMed Research International (2018) Vol. 2018, pp. 1-26
Open Access | Times Cited: 20
Automatic Detection of Focal Liver Lesions in Multi-phase CT Images Using A Multi-channel & Multi-scale CNN
Yoshihiro Todoroki, Yutaro Iwamoto, Lanfen Lin, et al.
(2019), pp. 872-875
Closed Access | Times Cited: 18
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