The DOI matches its Crossref record; 40 of the 41 references it cites were found in a registry or an index.
Detecting Anatomical Landmarks From Limited Medical Imaging Data Using Two-Stage Task-Oriented Deep Neural Networks
Open accessAbstract
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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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
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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
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