期刊
NEUROIMAGE
卷 166, 期 -, 页码 32-45出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2017.10.058
关键词
Hierarchical clustering; Normalized cuts; Tractography; Diffusion MRI
资金
- National Institute of Mental Health [U01-MH108168]
- National Institute of Biomedical Imaging and Bioengineering [R01EB021265, P41EB015896, 1R01EB023281, R01EB006758, R21EB018907, R01EB019956]
- National Institute on Aging [R01AG008122, R01AG016495]
- National Institute of Diabetes and Digestive and Kidney Diseases [R21DK108277]
- National Institute of Neurological Disorders and Stroke [R01NS0525851, R21NS072652, R01NS070963, R01NS083534, U01NS086625]
- Shared Instrumentation Grant [1S10RR023401, 1S10RR019307, 1S10RR023043]
- NIH Blueprint for Neuroscience Research [U01-MH093765]
- NIH Blueprint for Neuroscience Research part of the multi-institutional Human Connectome Project [T90DA022759/R90DA023427]
Diffusion MRI tractography produces massive sets of streamlines that contain a wealth of information on brain connections. The size of these datasets creates a need for automated clustering methods to group the streamlines into meaningful bundles. Conventional clustering techniques group streamlines based on their spatial coordinates. Neuroanatomists, however, define white-matter bundles based on the anatomical structures that they go through or next to, rather than their spatial coordinates. Thus we propose a similarity measure for clustering streamlines based on their position relative to cortical and subcortical brain regions. We incorporate this measure into a hierarchical clustering algorithm and compare it to a measure that relies on Euclidean distance, using data from the Human Connectome Project. We show that the anatomical similarity measure leads to a 20% improvement in the overlap of clusters with manually labeled tracts. Importantly, this is achieved without introducing any prior information from a tract atlas into the clustering algorithm, therefore without imposing the existence of any named tracts.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据