Chapter title |
3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients
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Chapter number | 25 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016
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Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, October 2016
|
DOI | 10.1007/978-3-319-46723-8_25 |
Pubmed ID | |
Book ISBNs |
978-3-31-946722-1, 978-3-31-946723-8
|
Authors |
Dong Nie, Han Zhang, Ehsan Adeli, Luyan Liu, Dinggang Shen, Nie, Dong, Zhang, Han, Adeli, Ehsan, Liu, Luyan, Shen, Dinggang |
Abstract |
High-grade glioma is the most aggressive and severe brain tumor that leads to death of almost 50% patients in 1-2 years. Thus, accurate prognosis for glioma patients would provide essential guidelines for their treatment planning. Conventional survival prediction generally utilizes clinical information and limited handcrafted features from magnetic resonance images (MRI), which is often time consuming, laborious and subjective. In this paper, we propose using deep learning frameworks to automatically extract features from multi-modal preoperative brain images (i.e., T1 MRI, fMRI and DTI) of high-grade glioma patients. Specifically, we adopt 3D convolutional neural networks (CNNs) and also propose a new network architecture for using multi-channel data and learning supervised features. Along with the pivotal clinical features, we finally train a support vector machine to predict if the patient has a long or short overall survival (OS) time. Experimental results demonstrate that our methods can achieve an accuracy as high as 89.9% We also find that the learned features from fMRI and DTI play more important roles in accurately predicting the OS time, which provides valuable insights into functional neuro-oncological applications. |
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Unknown | 1 | 100% |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Japan | 1 | <1% |
United States | 1 | <1% |
Pakistan | 1 | <1% |
Unknown | 251 | 99% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 49 | 19% |
Researcher | 37 | 15% |
Student > Master | 30 | 12% |
Student > Bachelor | 15 | 6% |
Other | 11 | 4% |
Other | 34 | 13% |
Unknown | 78 | 31% |
Readers by discipline | Count | As % |
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Computer Science | 52 | 20% |
Engineering | 42 | 17% |
Medicine and Dentistry | 29 | 11% |
Neuroscience | 13 | 5% |
Agricultural and Biological Sciences | 7 | 3% |
Other | 15 | 6% |
Unknown | 96 | 38% |