Chapter title |
Segmentation of Gliomas in Pre-operative and Post-operative Multimodal Magnetic Resonance Imaging Volumes Based on a Hybrid Generative-Discriminative Framework
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Chapter number | 18 |
Book title |
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
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Published in |
Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries : second International Workshop, BrainLes 2016, with the challenges on BRATS, ISLES and mTOP 2016, held in conjunction with MICCAI 2016, Athens, Greece, Octob..., October 2016
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DOI | 10.1007/978-3-319-55524-9_18 |
Pubmed ID | |
Book ISBNs |
978-3-31-955523-2, 978-3-31-955524-9
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Authors |
Ke Zeng, Spyridon Bakas, Aristeidis Sotiras, Hamed Akbari, Martin Rozycki, Saima Rathore, Sarthak Pati, Christos Davatzikos |
Abstract |
We present an approach for segmenting both low- and high-grade gliomas in multimodal magnetic resonance imaging volumes. The proposed framework is an extension of our previous work [6,7], with an additional component for segmenting post-operative scans. The proposed approach is based on a hybrid generative-discriminative model. Firstly, a generative model based on a joint segmentation-registration framework is used to segment the brain scans into cancerous and healthy tissues. Secondly, a gradient boosting classification scheme is used to refine tumor segmentation based on information from multiple patients. We evaluated our approach in 218 cases during the training phase of the BRAin Tumor Segmentation (BRATS) 2016 challenge and report promising results. During the testing phase, the proposed approach was ranked among the top performing methods, after being additionally evaluated in 191 unseen cases. |
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Portugal | 1 | 100% |
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Mendeley readers
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Unknown | 26 | 100% |
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Student > Master | 6 | 23% |
Student > Ph. D. Student | 3 | 12% |
Unspecified | 2 | 8% |
Lecturer | 2 | 8% |
Researcher | 2 | 8% |
Other | 2 | 8% |
Unknown | 9 | 35% |
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Computer Science | 6 | 23% |
Engineering | 4 | 15% |
Unspecified | 2 | 8% |
Medicine and Dentistry | 2 | 8% |
Neuroscience | 1 | 4% |
Other | 0 | 0% |
Unknown | 11 | 42% |