The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Estimating CT Image from MRI Data Using 3D Fully Convolutional Networks
Open accessAbstract
Computed tomography (CT) is critical for various clinical applications, e.g., radiotherapy treatment planning and also PET attenuation correction. However, CT exposes radiation during CT imaging, which may cause side effects to patients. Compared to CT, magnetic resonance imaging (MRI) is much safer and does not involve any radiation. Therefore, recently researchers are greatly motivated to estimate CT image from its corresponding MR image of the same subject for the case of radiotherapy planning. In this paper, we propose a 3D deep learning based method to address this challenging problem. Specifically, a 3D fully convolutional neural network (FCN) is adopted to learn an end-to-end nonlinear mapping from MR image to CT image. Compared to the conventional convolutional neural network (CNN), FCN generates structured output and can better preserve the neighborhood information in the predicted CT image. We have validated our method in a real pelvic CT/MRI dataset. Experimental results show that our method is accurate and robust for predicting CT image from MRI image, and also outperforms three state-of-the-art methods under comparison. In addition, the parameters, such as network depth and activation function, are extensively studied to give an insight for deep learning based regression tasks in our application.
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Learning Spatiotemporal Features with 3D Convolutional Networks
Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.510 · Cited by 9,593
Attenuation correction for a combined 3D PET/CT scanner
Kinahan, P. E., Townsend, D. W., Beyer, T., & Sashin, D. (1998) · Medical Physics · doi:10.1118/1.598392 · Cited by 946
Fully convolutional networks for multi-modality isointense infant brain image segmentation
Nie, D., Wang, L., Gao, Y., & Shen, D. (2016) · 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493515 · Cited by 304
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A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Deep MR to CT Synthesis Using Unpaired Data
Wolterink, J. M., Dinkla, A. M., Savenije, M. H. F., Seevinck, P. R., van den Berg, C. A. T., & Išgum, I. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-68127-6_2 · Cited by 593
Mazurowski, M. A., Buda, M., Saha, A., & Bashir, M. R. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26534 · Cited by 554
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
Trebeschi, S., van Griethuysen, J. J. M., Lambregts, D. M. J., Lahaye, M. J., Parmar, C., Bakers, F. C. H., Peters, N. H. G. M., Beets-Tan, R. G. H., & Aerts, H. J. W. L. (2017) · Scientific Reports · doi:10.1038/s41598-017-05728-9 · Cited by 292
Metadata
- Authors
- Nie, D., Cao, X., Gao, Y., Wang, L., & Shen, D.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Volume
- 2016
- Pages
- 170-178
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2523468284
- Cited by
- 256
- References
- 5
- FWCI
- 9.52
- Type
- conference paper
- Language
- en
- Open access
- green
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