TY - GEN
T1 - Estimating CT image from MRI data using 3D fully convolutional networks
AU - Nie, Dong
AU - Cao, Xiaohuan
AU - Gao, Yaozong
AU - Wang, Li
AU - Shen, Dinggang
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - 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 endto-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.
AB - 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 endto-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.
UR - http://www.scopus.com/inward/record.url?scp=84992513308&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46976-8_18
DO - 10.1007/978-3-319-46976-8_18
M3 - Conference contribution
AN - SCOPUS:84992513308
SN - 9783319469751
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 170
EP - 178
BT - Deep Learning and Data Labeling for Medical Applications - 1st International Workshop, LABELS 2016, and 2nd International Workshop, DLMIA 2016 Held in Conjunction with MICCAI 2016, Proceedings
A2 - Lu, Zhi
A2 - Belagiannis, Vasileios
A2 - Tavares, Joao Manuel R.S.
A2 - Cardoso, Jaime S.
A2 - Bradley, Andrew
A2 - Papa, Joao Paulo
A2 - Nascimento, Jacinto C.
A2 - Loog, Marco
A2 - Cornebise, Julien
A2 - Carneiro, Gustavo
A2 - Mateus, Diana
A2 - Peter, Loic
PB - Springer Verlag
T2 - 1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016 and 2nd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2016
Y2 - 21 October 2016 through 21 October 2016
ER -