Chapter title |
Holistic Mapping of Striatum Surfaces in the Laplace-Beltrami Embedding Space
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Chapter number | 3 |
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_3 |
Pubmed ID | |
Book ISBNs |
978-3-31-966181-0, 978-3-31-966182-7
|
Authors |
Jin Kyu Gahm, Yonggang Shi |
Abstract |
In brain shape analysis, the striatum is typically divided into three parts: the caudate, putamen, and accumbens nuclei for its analysis. Recent connectivity and animal studies, however, indicate striatum-cortical inter-connections do not always follow such subdivisions. For the holistic mapping of striatum surfaces, conventional spherical registration techniques are not suitable due to the large metric distortions in spherical parameterization of striatal surfaces. To overcome this difficulty, we develop a novel striatal surface mapping method using the recently proposed Riemannian metric optimization techniques in the Laplace-Beltrami (LB) embedding space. For the robust resolution of sign ambiguities in the LB spectrum, we also devise novel anatomical contextual features to guide the surface mapping in the embedding space. In our experimental results, we compare with spherical registration tools from FreeSurfer and FSL to demonstrate that our novel method provides a superior solution to the striatal mapping problem. We also apply our method to map the striatal surfaces from 211 subjects of the Human Connectome Project (HCP), and use the surface maps to construct a cortical connectivity atlas. Our atlas results show that the striato-cortical connectivity is not distinctive according to traditional structural subdivision of the striatum, and further confirms the holistic approach for mapping striatal surfaces. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 4 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 1 | 25% |
Student > Bachelor | 1 | 25% |
Student > Postgraduate | 1 | 25% |
Unknown | 1 | 25% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 2 | 50% |
Agricultural and Biological Sciences | 1 | 25% |
Unknown | 1 | 25% |