Chapter title |
4D Infant Cortical Surface Atlas Construction Using Spherical Patch-Based Sparse Representation
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Chapter number | 7 |
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 | 10.1007/978-3-319-66182-7_7 |
Pubmed ID | |
Book ISBNs |
978-3-31-966181-0, 978-3-31-966182-7
|
Authors |
Zhengwang Wu, Gang Li, Yu Meng, Li Wang, Weili Lin, Dinggang Shen |
Abstract |
The 4D infant cortical surface atlas with densely sampled time points is highly needed for neuroimaging analysis of early brain development. In this paper, we build the 4D infant cortical surface atlas firstly covering 6 postnatal years with 11 time points (i.e., 1, 3, 6, 9, 12, 18, 24, 36, 48, 60, and 72 months), based on 339 longitudinal MRI scans from 50 healthy infants. To build the 4D cortical surface atlas, first, we adopt a two-stage groupwise surface registration strategy to ensure both longitudinal consistency and unbiasedness. Second, instead of simply averaging over the co-registered surfaces, a spherical patch-based sparse representation is developed to overcome possible surface registration errors across different subjects. The central idea is that, for each local spherical patch in the atlas space, we build a dictionary, which includes the samples of current local patches and their spatially-neighboring patches of all co-registered surfaces, and then the current local patch in the atlas is sparsely represented using the built dictionary. Compared to the atlas built with the conventional methods, the 4D infant cortical surface atlas constructed by our method preserves more details of cortical folding patterns, thus leading to boosted accuracy in registration of new infant cortical surfaces. |
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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 | 3 | 25% |
Student > Master | 3 | 25% |
Student > Bachelor | 1 | 8% |
Other | 1 | 8% |
Student > Postgraduate | 1 | 8% |
Other | 0 | 0% |
Unknown | 3 | 25% |
Readers by discipline | Count | As % |
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Nursing and Health Professions | 1 | 8% |
Agricultural and Biological Sciences | 1 | 8% |
Medicine and Dentistry | 1 | 8% |
Neuroscience | 1 | 8% |
Other | 1 | 8% |
Unknown | 4 | 33% |