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Detecting Anatomical Landmarks From Limited Medical Imaging Data Using Two-Stage Task-Oriented Deep Neural Networks
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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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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
TensorFlow: A system for large-scale machine learningPreprint
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
R-FCN: Object Detection via Region-based Fully Convolutional NetworksPreprint
Dai, J., Li, Y., He, K., & Sun, J. Z. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06409 · Cited by 5,135
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6229 · Cited by 2,680
Facial Landmark Detection by Deep Multi-task Learning
Zhang, Z., Luo, P., Loy, C. C., & Tang, X. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10599-4_7 · Cited by 1,410
Deep Convolutional Network Cascade for Facial Point Detection
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
Efficient object localization using Convolutional Networks
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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Deep learning in medical imaging and radiation therapy
Sahiner, B., Pezeshk, A. X., Hadjiiski, L. M., Wang, X., Drukker, K., Cha, K. H., Summers, R. M., & Giger, M. L. (2018) · Medical Physics · doi:10.1002/mp.13264 · Cited by 763
Lian, C., Liu, M., Zhang, J., & Shen, D. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2889096 · Cited by 544
Self-citationConvolutional Neural Networks for Radiologic Images: A Radiologist’s Guide
Soffer, S., Ben-Cohen, A., Shimon, O., Amitai, M. M., Greenspan, H. K., & Klang, E. (2019) · Radiology · doi:10.1148/radiol.2018180547 · Cited by 528
Landmark-based deep multi-instance learning for brain disease diagnosis
Liu, M., Zhang, J., Adeli, E., & Shen, D. G. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.10.005 · Cited by 434
Self-citationLiang, S., Wu, H., Zhen, L., Hua, Q., Garg, S., Kaddoum, G., Hassan, M. M., & Yu, K. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3158253 · Cited by 367
Integrating spatial configuration into heatmap regression based CNNs for landmark localization
Payer, C., Štern, D., Bischof, H., & Urschler, M. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.03.007 · Cited by 377
Survey on deep learning for radiotherapy
Meyer, P., Noblet, V., Mazzara, C., & Lallement, A. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.018 · Cited by 302
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions
Altaf, F., Islam, S. M. S., Akhtar, N., & Janjua, N. K. (2019) · IEEE Access · doi:10.1109/access.2019.2929365 · Cited by 301
A brief review on multi-task learning
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One-Pass Multi-Task Networks With Cross-Task Guided Attention for Brain Tumor Segmentation
Zhou, C., Ding, C., Wang, X., Lu, Z., & Tao, D. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2973510 · Cited by 256
High-Resolution Encoder–Decoder Networks for Low-Contrast Medical Image Segmentation
Zhou, S., Nie, D., Adeli, E., Yin, J., Lian, J., & Shen, D. G. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2919937 · Cited by 230
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Weakly Supervised Segmentation of COVID19 Infection with Scribble Annotation on CT Images
Liu, X., Yuan, Q., Gao, Y., He, K., Wang, S., Tang, X., Tang, J., & Shen, D. G. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108341 · Cited by 193
Self-citationWang, G., Liu, X., Shen, J., Wang, C., Li, Z., Ye, L., Wu, X., Chen, T., Wang, K., Zhang, X., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00704-1 · Cited by 202
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Alansary, A., Oktay, O., Li, Y., Folgoc, L. L., Hou, B., Vaillant, G., Kamnitsas, K., Vlontzos, A., Glocker, B., Kainz, B., et al. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.02.007 · Cited by 193
Zhang, J., Saha, A., Zhu, Z., & Mazurowski, M. A. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2865671 · Cited by 175
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Lian, C., Zhang, J., Liu, M., Zong, X., Hung, S., Lin, W., & Shen, D. G. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.02.009 · Cited by 127
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Self-citationWang, S., Cong, Y., Zhu, H., Chen, X., Qu, L., Fan, H., Zhang, Q., & Liu, M. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2997760 · Cited by 121
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Hu, Y., Wen, G., Liao, H., Wang, C., Dai, D., & Yu, Z. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2909925 · Cited by 60
Effectiveness of Human–Artificial Intelligence Collaboration in Cephalometric Landmark Detection
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Xue, T., Zhang, F., Zhang, C., Chen, Y., Song, Y., Golby, A. J., Makris, N., Yogesh Rathi, A., Cai, W., & O’Donnell, L. J. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102759 · Cited by 58
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Lang, Y., Lian, C., Xiao, D., Deng, H., Thung, K., Yuan, P., Gateño, J., Kuang, T., Alfi, D. M., Wang, L., et al. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3174513 · Cited by 47
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Sun, L., Zhang, D., Lian, C., Wang, L., Wu, Z., Shao, W., Lin, W., Shen, D. G., & Li, G. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.05.037 · Cited by 26
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Fu, Z., Jiao, J., Suttie, M., & Noble, J. A. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3110680 · Cited by 19
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Shi, J., Zhang, R., Guo, L., Gao, L., Ma, H., & Wang, J. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2994114 · Cited by 16
Liu, Y., Pan, Y., Yang, W., Ning, Z., Yue, L., Liu, M., & Shen, D. G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59728-3_57 · Cited by 16
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Li, J., Wang, Y., Mao, J., Li, G., & Ma, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59861-7_63 · Cited by 14
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Wang, X., Zhai, S., & Niu, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.02.069 · Cited by 13
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Self-citationXu, D., Ma, M., Cao, M., Kishan, A. U., Nickols, N. G., Scalzo, F., & Sheng, K. (2023) · Scientific Reports · doi:10.1038/s41598-023-28669-y · Cited by 12
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Metadata
- Authors
- Zhang, J., Liu, M., & Shen, D.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Venue
- IEEE Transactions on Image Processing
- Published
- 2017-10
- Volume
- 26
- Issue
- 10
- Pages
- 4753-4764
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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