Chapter title |
Medical Image Synthesis with Context-Aware Generative Adversarial Networks
|
---|---|
Chapter number | 48 |
Book title |
Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017
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Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, September 2017
|
DOI | 10.1007/978-3-319-66179-7_48 |
Pubmed ID | |
Book ISBNs |
978-3-31-966178-0, 978-3-31-966179-7
|
Authors |
Dong Nie, Roger Trullo, Jun Lian, Caroline Petitjean, Su Ruan, Qian Wang, Dinggang Shen, Nie, Dong, Trullo, Roger, Lian, Jun, Petitjean, Caroline, Ruan, Su, Wang, Qian, Shen, Dinggang |
Abstract |
Computed tomography (CT) is critical for various clinical applications, e.g., radiation treatment planning and also PET attenuation correction in MRI/PET scanner. However, CT exposes radiation during acquisition, which may cause side effects to patients. Compared to CT, magnetic resonance imaging (MRI) is much safer and does not involve radiations. Therefore, recently researchers are greatly motivated to estimate CT image from its corresponding MR image of the same subject for the case of radiation planning. In this paper, we propose a data-driven approach to address this challenging problem. Specifically, we train a fully convolutional network (FCN) to generate CT given the MR image. To better model the nonlinear mapping from MRI to CT and produce more realistic images, we propose to use the adversarial training strategy to train the FCN. Moreover, we propose an image-gradient-difference based loss function to alleviate the blurriness of the generated CT. We further apply Auto-Context Model (ACM) to implement a context-aware generative adversarial network. Experimental results show that our method is accurate and robust for predicting CT images from MR images, and also outperforms three state-of-the-art methods under comparison. |
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United States | 1 | 50% |
Netherlands | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 50% |
Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
Geographical breakdown
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Germany | 1 | <1% |
Austria | 1 | <1% |
Unknown | 561 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 130 | 23% |
Student > Master | 93 | 17% |
Researcher | 87 | 15% |
Student > Bachelor | 40 | 7% |
Student > Doctoral Student | 20 | 4% |
Other | 52 | 9% |
Unknown | 141 | 25% |
Readers by discipline | Count | As % |
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Computer Science | 195 | 35% |
Engineering | 91 | 16% |
Medicine and Dentistry | 29 | 5% |
Physics and Astronomy | 21 | 4% |
Neuroscience | 12 | 2% |
Other | 41 | 7% |
Unknown | 174 | 31% |