The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network
Open accessAbstract
Given the potential risk of X-ray radiation to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. Currently, the main stream low-dose CT methods include vendor-specific sinogram domain filtration and iterative reconstruction algorithms, but they need to access raw data, whose formats are not transparent to most users. Due to the difficulty of modeling the statistical characteristics in the image domain, the existing methods for directly processing reconstructed images cannot eliminate image noise very well while keeping structural details. Inspired by the idea of deep learning, here we combine the autoencoder, deconvolution network, and shortcut connections into the residual encoder-decoder convolutional neural network (RED-CNN) for low-dose CT imaging. After patch-based training, the proposed RED-CNN achieves a competitive performance relative to the-state-of-art methods in both simulated and clinical cases. Especially, our method has been favorably evaluated in terms of noise suppression, structural preservation, and lesion detection.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
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
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Image Super-Resolution Using Deep Convolutional Networks
Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105
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Applications of machine learning in drug discovery and development
Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0024-5 · Cited by 3,228
Yang, Q., Yan, P., Zhang, Y., Yu, H., Shi, Y., Mou, X., Kalra, M. K., Zhang, Y., Sun, L., & Wang, G. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2827462 · Cited by 1,630
Self-citationDeep learning with convolutional neural network in radiology
Yasaka, K., Akai, H., Kunimatsu, A., Kiryu, S., & Abe, O. (2018) · Japanese Journal of Radiology · doi:10.1007/s11604-018-0726-3 · Cited by 368
Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network
Kang, E., Chang, W., Yoo, J., & Ye, J. C. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2823756 · Cited by 336
Li, M., Hsu, W., Xie, X., Cong, J., & Gao, W. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2968472 · Cited by 321
Metadata
- Authors
- Chen, H., Zhang, Y., Kalra, M. K., Lin, F., Chen, Y., Liao, P., Zhou, J., & Wang, G.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2017-12
- Volume
- 36
- Issue
- 12
- Pages
- 2524-2535
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2584483805
- Cited by
- 1,866
- References
- 8
- FWCI
- 49.08
- Type
- journal article
- Language
- en
- Open access
- green
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- Chen, H., Zhang, Y., Kalra, M. K., Lin, F., Chen, Y., Liao, P., Zhou, J., & Wang, G. (2017). Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2017.2715284
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