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3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network.
IEEE Trans Med Imaging 2018; 37(6):1522-1534IT

Abstract

Low-dose computed tomography (LDCT) has attracted major attention in the medical imaging field, since CT-associated X-ray radiation carries health risks for patients. The reduction of the CT radiation dose, however, compromises the signal-to-noise ratio, which affects image quality and diagnostic performance. Recently, deep-learning-based algorithms have achieved promising results in LDCT denoising, especially convolutional neural network (CNN) and generative adversarial network (GAN) architectures. This paper introduces a conveying path-based convolutional encoder-decoder (CPCE) network in 2-D and 3-D configurations within the GAN framework for LDCT denoising. A novel feature of this approach is that an initial 3-D CPCE denoising model can be directly obtained by extending a trained 2-D CNN, which is then fine-tuned to incorporate 3-D spatial information from adjacent slices. Based on the transfer learning from 2-D to 3-D, the 3-D network converges faster and achieves a better denoising performance when compared with a training from scratch. By comparing the CPCE network with recently published work based on the simulated Mayo data set and the real MGH data set, we demonstrate that the 3-D CPCE denoising model has a better performance in that it suppresses image noise and preserves subtle structures.

Authors

No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

29870379

Citation

Shan, Hongming, et al. "3-D Convolutional Encoder-Decoder Network for Low-Dose CT Via Transfer Learning From a 2-D Trained Network." IEEE Transactions On Medical Imaging, vol. 37, no. 6, 2018, pp. 1522-1534.
Shan H, Zhang Y, Yang Q, et al. 3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network. IEEE Trans Med Imaging. 2018;37(6):1522-1534.
Shan, H., Zhang, Y., Yang, Q., Kruger, U., Kalra, M. K., Sun, L., ... Wang, G. (2018). 3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network. IEEE Transactions On Medical Imaging, 37(6), pp. 1522-1534. doi:10.1109/TMI.2018.2832217.
Shan H, et al. 3-D Convolutional Encoder-Decoder Network for Low-Dose CT Via Transfer Learning From a 2-D Trained Network. IEEE Trans Med Imaging. 2018;37(6):1522-1534. PubMed PMID: 29870379.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - 3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network. AU - Shan,Hongming, AU - Zhang,Yi, AU - Yang,Qingsong, AU - Kruger,Uwe, AU - Kalra,Mannudeep K, AU - Sun,Ling, AU - Cong,Wenxiang, AU - Wang,Ge, PY - 2018/6/6/entrez PY - 2018/6/6/pubmed PY - 2019/5/21/medline SP - 1522 EP - 1534 JF - IEEE transactions on medical imaging JO - IEEE Trans Med Imaging VL - 37 IS - 6 N2 - Low-dose computed tomography (LDCT) has attracted major attention in the medical imaging field, since CT-associated X-ray radiation carries health risks for patients. The reduction of the CT radiation dose, however, compromises the signal-to-noise ratio, which affects image quality and diagnostic performance. Recently, deep-learning-based algorithms have achieved promising results in LDCT denoising, especially convolutional neural network (CNN) and generative adversarial network (GAN) architectures. This paper introduces a conveying path-based convolutional encoder-decoder (CPCE) network in 2-D and 3-D configurations within the GAN framework for LDCT denoising. A novel feature of this approach is that an initial 3-D CPCE denoising model can be directly obtained by extending a trained 2-D CNN, which is then fine-tuned to incorporate 3-D spatial information from adjacent slices. Based on the transfer learning from 2-D to 3-D, the 3-D network converges faster and achieves a better denoising performance when compared with a training from scratch. By comparing the CPCE network with recently published work based on the simulated Mayo data set and the real MGH data set, we demonstrate that the 3-D CPCE denoising model has a better performance in that it suppresses image noise and preserves subtle structures. SN - 1558-254X UR - https://www.unboundmedicine.com/medline/citation/29870379/3_D_Convolutional_Encoder_Decoder_Network_for_Low_Dose_CT_via_Transfer_Learning_From_a_2_D_Trained_Network_ L2 - https://dx.doi.org/10.1109/TMI.2018.2832217 DB - PRIME DP - Unbound Medicine ER -