Abstract
DTI tractography allows unprecedented understanding of brain neural connectivity in-vivo by capturing water diffusion patterns in brain white-matter microstructures. However, tractography algorithms often output hundreds of thousands of fibers, rendering the computation needed for subsequent data analysis intractable. A remedy is to group the fibers into bundles using fiber clustering techniques. Most existing fiber clustering methods, however, rely on fiber geometrical information only by viewing fibers as curves in the 3D Euclidean space. The important neuroanatomical aspect of the fibers is mostly ignored. In this paper, neuroanatomical information is encapsulated in a feature vector called the associativity vector, which functions as the "fingerprint" for each fiber and depicts the connectivity of the fiber with respect to individual anatomies. Using the associativity vectors of fibers, we model the fibers as observations sampled from multivariate Gaussian mixtures in the feature space. An expectation-maximization clustering approach is then employed to group the fibers into 16 major bundles. Experimental results indicate that the proposed method groups the fibers into anatomically meaningful bundles, which are highly consistent across subjects.
| Original language | English |
|---|---|
| Title of host publication | Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention |
| Pages | 17-24 |
| Number of pages | 8 |
| Volume | 14 |
| Edition | Pt 2 |
| Publication status | Published - 2011 Dec 1 |
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ASJC Scopus subject areas
- Medicine(all)
Cite this
Fiber modeling and clustering based on neuroanatomical features. / Wang, Qian; Yap, Pew Thian; Wu, Guorong; Shen, Dinggang.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 14 Pt 2. ed. 2011. p. 17-24.Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Fiber modeling and clustering based on neuroanatomical features.
AU - Wang, Qian
AU - Yap, Pew Thian
AU - Wu, Guorong
AU - Shen, Dinggang
PY - 2011/12/1
Y1 - 2011/12/1
N2 - DTI tractography allows unprecedented understanding of brain neural connectivity in-vivo by capturing water diffusion patterns in brain white-matter microstructures. However, tractography algorithms often output hundreds of thousands of fibers, rendering the computation needed for subsequent data analysis intractable. A remedy is to group the fibers into bundles using fiber clustering techniques. Most existing fiber clustering methods, however, rely on fiber geometrical information only by viewing fibers as curves in the 3D Euclidean space. The important neuroanatomical aspect of the fibers is mostly ignored. In this paper, neuroanatomical information is encapsulated in a feature vector called the associativity vector, which functions as the "fingerprint" for each fiber and depicts the connectivity of the fiber with respect to individual anatomies. Using the associativity vectors of fibers, we model the fibers as observations sampled from multivariate Gaussian mixtures in the feature space. An expectation-maximization clustering approach is then employed to group the fibers into 16 major bundles. Experimental results indicate that the proposed method groups the fibers into anatomically meaningful bundles, which are highly consistent across subjects.
AB - DTI tractography allows unprecedented understanding of brain neural connectivity in-vivo by capturing water diffusion patterns in brain white-matter microstructures. However, tractography algorithms often output hundreds of thousands of fibers, rendering the computation needed for subsequent data analysis intractable. A remedy is to group the fibers into bundles using fiber clustering techniques. Most existing fiber clustering methods, however, rely on fiber geometrical information only by viewing fibers as curves in the 3D Euclidean space. The important neuroanatomical aspect of the fibers is mostly ignored. In this paper, neuroanatomical information is encapsulated in a feature vector called the associativity vector, which functions as the "fingerprint" for each fiber and depicts the connectivity of the fiber with respect to individual anatomies. Using the associativity vectors of fibers, we model the fibers as observations sampled from multivariate Gaussian mixtures in the feature space. An expectation-maximization clustering approach is then employed to group the fibers into 16 major bundles. Experimental results indicate that the proposed method groups the fibers into anatomically meaningful bundles, which are highly consistent across subjects.
UR - http://www.scopus.com/inward/record.url?scp=82255193263&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=82255193263&partnerID=8YFLogxK
M3 - Chapter
C2 - 21995008
AN - SCOPUS:82255193263
VL - 14
SP - 17
EP - 24
BT - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
ER -