Cited by 192Medical Imaging and AnalysisMedical Image Segmentation TechniquesAdvanced Neural Network Applications

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Detecting Anatomical Landmarks From Limited Medical Imaging Data Using Two-Stage Task-Oriented Deep Neural Networks

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Zhang, Jun, Liu, Mingxia, Shen, Dinggang·2017·IEEE Transactions on Image Processing

DOI 10.1109/tip.2017.2721106PMC PMC5729285

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Abstract

One of the major challenges in anatomical landmark detection, based on deep neural networks, is the limited availability of medical imaging data for network learning. To address this problem, we present a two-stage task-oriented deep learning method to detect large-scale anatomical landmarks simultaneously in real time, using limited training data. Specifically, our method consists of two deep convolutional neural networks (CNN), with each focusing on one specific task. Specifically, to alleviate the problem of limited training data, in the first stage, we propose a CNN based regression model using millions of image patches as input, aiming to learn inherent associations between local image patches and target anatomical landmarks. To further model the correlations among image patches, in the second stage, we develop another CNN model, which includes a) a fully convolutional network that shares the same architecture and network weights as the CNN used in the first stage and also b) several extra layers to jointly predict coordinates of multiple anatomical landmarks. Importantly, our method can jointly detect large-scale (e.g., thousands of) landmarks in real time. We have conducted various experiments for detecting 1200 brain landmarks from the 3D T1-weighted magnetic resonance images of 700 subjects, and also 7 prostate landmarks from the 3D computed tomography images of 73 subjects. The experimental results show the effectiveness of our method regarding both accuracy and efficiency in the anatomical landmark detection.

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Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,548
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OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
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Sun, Y. J., Wang, X., & Tang, X. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.446 · Cited by 1,405
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Tompson, J., Goroshin, R., Jain, A., LeCun, Y., & Bregler, C. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298664 · Cited by 1,365

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Chen, H., Wang, R., Wang, X., Li, J., Fang, Q., Li, H., Bai, J., Peng, Q., Meng, D., & Wang, L. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3299038 · Cited by 7
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Wang, F., Lian, C., Véra, P., & Ruan, S. (2017) · Multimedia Systems · doi:10.1007/s00530-017-0579-0 · Cited by 7
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Rong, Y., Jiang, Z., Wu, W., Chen, Q., Wei, C., Fan, Z., & Chen, H. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11113203 · Cited by 7
Automation of Clinical Measurements on Radiographs of Children’s Hips
Thompson, P. L., Collaborative, M. A., Perry, D. C., Cootes, T. F., & Lindner, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16437-8_40 · Cited by 7
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Kim, J. H. (2019) · Journal of the Korean Society of Radiology · doi:10.3348/jksr.2019.80.2.176 · Cited by 7
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Automatic Detection of Anatomical Landmarks on Geometric Mesh Data using Deep Semantic Segmentation
Liu, S., He, J.-L., & Liao, S. (2020) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme46284.2020.9102920 · Cited by 6
Reinforced Redetection of Landmark in Pre- and Post-operative Brain Scan Using Anatomical Guidance for Image Alignment
Waldmannstetter, D., Navarro, F., Wiestler, B., Kirschke, J. S., Sekuboyina, A., Molero, E., & Menze, B. H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50120-4_8 · Cited by 6
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Wang, Z., Vandersteen, C., Raffaelli, C., Guevara, N., Patou, F., & Delingette, H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88210-5_15 · Cited by 6
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Wu, Z., Zhang, R., Bai, H., Ma, J., Ma, X., & Zhu, X. (2022) · The Knee · doi:10.1016/j.knee.2022.11.026 · Cited by 5
A permissible region extraction based on a knowledge priori for X-ray luminescence computed tomography
Yi, H., Qu, X., Sun, Y., Peng, J., Hou, Y., & He, X. (2017) · Multimedia Systems · doi:10.1007/s00530-017-0576-3 · Cited by 5
Cross-Task Representation Learning for Anatomical Landmark Detection
Fu, Z., Jiao, J., Suttie, M., & Noble, J. A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59861-7_59 · Cited by 5
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Ye, Y., Liu, Q., Li, L., Zhang, Z., Xu, L. P., Chu, J., & Wen, B. (2023) · Journal of Electronic Imaging · doi:10.1117/1.jei.32.6.063021 · Cited by 4
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Bai, T., Liu, S., Wang, Y., Wang, Y., & Dong, D. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669420 · Cited by 3
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Huang, K., & Feng, F. (2022) · Applied Sciences · doi:10.3390/app122010190 · Cited by 3
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Fan, Y., & Wang, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59719-1_76 · Cited by 3
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El Morr, C., & Ali-Hassan, H. (2019) · SpringerBriefs in Health Care Management and Economics · doi:10.1007/978-3-030-04506-7_2 · Cited by 2
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Salari, S., Harirpoush, A., Rivaz, H., & Xiao, Y. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01951 · Cited by 2
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Wu, H. Y., Xie, H., Lin, F., Zhang, S., Sun, J., & Zhang, Y. (2019) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3338533.3366574 · Cited by 2
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Raiaan, M. A. K., Rahman, M. A., Azam, S., Yeo, K. C., Sebastian, Y., & Jonkman, M. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111265 · Cited by 1
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Wang, Y., Lenarz, T., Kral, A., & John, S. (2023) · International Conference on Bioinformatics Research and Applications (ICBRA) · doi:10.1145/3632047.3632048 · Cited by 1
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Yazdi, A. S., Cappella, A., Baldini, B., Solazzo, R., Tartaglia, G. M., Sforza, C., & Baselli, G. (2025) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2025.101729 · Cited by 1
Recent Advances and Challenges in Facial Emotion Recognition Systems: A Review of Deep Learning Approaches
Zeqo, E., Bakiasi, V., (Lamaj), B. M., & Dyrkaj, F. (2026) · International Journal for Research Trends and Innovation · doi:10.56975/ijrti.v11i5.212555 · Cited by 1
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Caspersen, M., Tanveer, M. S., Shihavuddin, A., Syeed, M. M. M., Maruf, M. H., Amin, A., & Uddin, M. F. (2022) · Multidimensional Signal Processing Workshop · doi:10.1109/ivmsp54334.2022.9816249 · Cited by 1
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Salunkhe, S. P., & Suryawanshi, N. A. (2025) · Research on Biomedical Engineering · doi:10.1007/s42600-024-00394-z · Cited by 1
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Geidarov, N. A., Klyshnikov, K. Y., & Ovcharenko, E. A. (2020) · Biomedical Engineering · doi:10.1007/s10527-020-09961-x · Cited by 1
[Coupled convolutional and graph network-based diagnosis of Alzheimer's disease using MRI].
Li, Q., Xing, X., & Feng, Q. (2020) · PubMed Central · doi:10.12122/j.issn.1673-4254.2020.04.13 · Cited by 1
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Visual computing of dissected aortae
Pepe, A., Fleischmann, D., Schmalstieg, D., & Egger, J. (2020) · doi:10.13140/rg.2.2.24092.92800 · Cited by 1
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Pepe, A., Trotta, G. F., Gsaxner, C., Brunetti, A., Cascarano, G. D., Bevilacqua, V., Shen, D. G., & Egger, J. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823299-6.00003-1 · Cited by 1
Self-citation
Machine Learning for Craniomaxillofacial Landmark Digitization of 3D Imaging
Zhang, J., Liu, M., Wang, L., Lian, C., & Shen, D. G. (2021) · Springer eBooks · doi:10.1007/978-3-030-71881-7_2 · Cited by 1
Self-citation
Anthropometric salient points and convolutional neural network (CNN) for 3D human body classification
Basu, S., Li, C., & Cohen, F. S. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12284-6 · Cited by 1
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Retracted
Joint Segmentation and Sub-pixel Localization in Structured Light Laryngoscopy
Henningson, J.-O., Semmler, M., Döllinger, M., & Stamminger, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43987-2_4 · Cited by 1
An Effective Fuzzy Neural Network with Reinforcement Learning Approach for Medical Data Classification
Prasad, P. P., Francis, F. S., & Huq, S. (2019) · International journal of intelligent engineering and systems · doi:10.22266/ijies2020.0229.32
Automating Interpretation of Images and Visual Inspections in Modern Manufacturing and Medical Settings
Rasoolian, B. (2021)
COVID-19–affected medical image analysis using DenserNet
Adak, C., Ghosh, D., Chowdhury, R. R., & Chattopadhyay, S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-824536-1.00021-6
An Augmentation Strategy with LightweightNetwork for Polyp Segmentation.
Ghimire, R., Poudel, S., & Lee, S.-W. (2021)
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Morris, E. L. (2021) · Deep Blue (University of Michigan) · doi:10.7302/2729
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Luo, H., Rashid, S. R., Nie, Y., Chen, A., Li, C., Wu, S., Zheng, Z., Cheng, Z., Castañeda, B., Peng, B., et al. (2023) · International Conference on Pattern Recognition and Artificial Intelligence (PRAI) · doi:10.1109/prai59366.2023.10331970
U-Net based on multi-head attention mechanism
Zhang, G., Lu, Y., Qiu, T., & Li, W. (2023) · International Conference on Signal Processing and Computer Science (SPCS) · doi:10.1117/12.3012314
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Deepali, G., Anitha, H., Swathi, B. P., & Suhas, M. V. (2025) · IEEE Access · doi:10.1109/access.2025.3582273
EAGLE-Net: A hierarchical neural network for detecting anatomical landmarks in upper gastrointestinal endoscopy for clinical diagnosis
Nguyen, T. T. H., Truong, V. D., Manh, X. H., Nguyen, T. T., Dao, H. V., & Vu, H. (2025) · Healthcare Analytics · doi:10.1016/j.health.2025.100420
Long-term stabilized iris tracking with unsupervised constraints on dynamic AS-OCT
Hu, L., Wu, X., Higashita, R., Xing, X., Zhou, M., Lin, S., Li, X., Yue, Y., Xiao, Z., Zhang, Y., et al. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103787
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Ryu, S. M., Park, C., Lee, J., Kim, K., Kim, S., Shin, S., Kim, S., Shin, K., & Koh, K. H. (2026) · Medical Physics · doi:10.1002/mp.70285
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Choi, S.-B., Ham, G.-S., & Oh, K. (2026) · Electronics · doi:10.3390/electronics15030589
Augmented reality based middle and inner ear surgical procedures
Hussain, R. (2020) · doi:10.70675/0c0915bez569cz441aza859zc33026a18f13
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Authors: Zhang, J., Liu, M., & Shen, D.
Affiliations: University of North Carolina at Chapel Hill (US)
ORCID: Zhang, Jun
Liu, Mingxia
Shen, Dinggang
Venue: IEEE Transactions on Image Processing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2017-10
Volume: 26
Issue: 10
Pages: 4753-4764
ISSN: 1057-7149 (ISSN-L)
1941-0042
DOI: 10.1109/tip.2017.2721106
PMC: PMC5729285
OpenAlex: W2724710774
Cited by: 192
References: 41
Citations by year: 2026: 10 · 2025: 14 · 2024: 11 · 2023: 16 · 2022: 31 · 2021: 29 · 2020: 29 · 2019: 20 · 2018: 26 · 2017: 6
FWCI: 9.98
Type: journal article
Language: en
Open access: green
europepmc.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Transactions on Image Processing (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
Figshare (submittedVersion, cc-by)
UNC Libraries
Funders: NIH (EB006733, EB008374, EB009634, MH100217, AG041721, AG049371, AG042599)
Topics: Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: anatomical landmark detection, limited training data, convolutional neural networks, fully convolutional network, 3D medical images, T1-weighted imaging, CT images

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Detecting Anatomical Landmarks From Limited Medical Imaging Data Using Two-Stage Task-Oriented Deep Neural Networks

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