Chapter title |
Multi-scale and Multimodal Fusion of Tract-tracing, Myelin Stain and DTI-derived Fibers in Macaque Brains.
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Chapter number | 30 |
Book title |
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015
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Published in |
Lecture notes in computer science, October 2015
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DOI | 10.1007/978-3-319-24571-3_30 |
Pubmed ID | |
Book ISBNs |
978-3-31-924570-6, 978-3-31-924571-3
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Authors |
Zhang, Tuo, Kong, Jun, Jing, Ke, Chen, Hanbo, Jiang, Xi, Li, Longchuan, Guo, Lei, Lu, Jianfeng, Hu, Xiaoping, Liu, Tianming, Tuo Zhang, Jun Kong, Ke Jing, Hanbo Chen, Xi Jiang, Longchuan Li, Lei Guo, Jianfeng Lu, Xiaoping Hu, Tianming Liu |
Abstract |
Assessment of structural connectivity patterns of brains can be an important avenue for better understanding mechanisms of structural and functional brain architectures. Therefore, many efforts have been made to estimate and validate axonal pathways via a number of techniques, such as myelin stain, tract-tracing and diffusion MRI (dMRI). The three modalities have their own advantages and are complimentary to each other. From myelin stain data, we can infer rich in-plane information of axonal orientation at micro-scale. Tract-tracing data is considered as 'gold standard' to estimate trustworthy meso-scale pathways. dMRI currently is the only way to estimate global macro-scale pathways given further validation. We propose a framework to take advantage of these three modalities. Information of the three modalities is integrated to determine the optimal tractography parameters for dMRI fibers and identify cross-validated fiber bundles that are finally used to construct atlas. We demonstrate the effectiveness of the framework by a collection of experimental results. |
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Geographical breakdown
Country | Count | As % |
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Unknown | 12 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 4 | 33% |
Other | 2 | 17% |
Student > Doctoral Student | 2 | 17% |
Student > Master | 1 | 8% |
Researcher | 1 | 8% |
Other | 2 | 17% |
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Computer Science | 2 | 17% |
Biochemistry, Genetics and Molecular Biology | 1 | 8% |
Physics and Astronomy | 1 | 8% |
Psychology | 1 | 8% |
Other | 2 | 17% |
Unknown | 1 | 8% |