TY - GEN
T1 - Automatic population HARDI white matter tract clustering by label fusion of multiple tract atlases
AU - Jin, Yan
AU - Shi, Yonggang
AU - Zhan, Liang
AU - Li, Junning
AU - De Zubicaray, Greig I.
AU - McMahon, Katie L.
AU - Martin, Nicholas G.
AU - Wright, Margaret J.
AU - Thompson, Paul M.
N1 - Funding Information:
This study was supported by Grant RO1 HD050735 from the National Institutes of Health (NIH) and Grant 496682 from the National Health and Medical Research Council (NHMRC), Australia.
PY - 2012
Y1 - 2012
N2 - Automatic labeling of white matter fibres in diffusion-weighted brain MRI is vital for comparing brain integrity and connectivity across populations, but is challenging. Whole brain tractography generates a vast set of fibres throughout the brain, but it is hard to cluster them into anatomically meaningful tracts, due to wide individual variations in the trajectory and shape of white matter pathways. We propose a novel automatic tract labeling algorithm that fuses information from tractography and multiple hand-labeled fibre tract atlases. As streamline tractography can generate a large number of false positive fibres, we developed a top-down approach to extract tracts consistent with known anatomy, based on a distance metric to multiple hand-labeled atlases. Clustering results from different atlases were fused, using a multi-stage fusion scheme. Our "label fusion" method reliably extracted the major tracts from 105-gradient HARDI scans of 100 young normal adults.
AB - Automatic labeling of white matter fibres in diffusion-weighted brain MRI is vital for comparing brain integrity and connectivity across populations, but is challenging. Whole brain tractography generates a vast set of fibres throughout the brain, but it is hard to cluster them into anatomically meaningful tracts, due to wide individual variations in the trajectory and shape of white matter pathways. We propose a novel automatic tract labeling algorithm that fuses information from tractography and multiple hand-labeled fibre tract atlases. As streamline tractography can generate a large number of false positive fibres, we developed a top-down approach to extract tracts consistent with known anatomy, based on a distance metric to multiple hand-labeled atlases. Clustering results from different atlases were fused, using a multi-stage fusion scheme. Our "label fusion" method reliably extracted the major tracts from 105-gradient HARDI scans of 100 young normal adults.
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U2 - 10.1007/978-3-642-33530-3_12
DO - 10.1007/978-3-642-33530-3_12
M3 - Conference contribution
C2 - 26207263
AN - SCOPUS:84868251794
SN - 9783642335297
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 147
EP - 156
BT - Multimodal Brain Image Analysis - Second International Workshop, MBIA 2012, Held in Conjunction with MICCAI 2012, Proceedings
T2 - 2nd International Workshop on Multimodal Brain Image Analysis, MBIA 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
Y2 - 1 October 2012 through 5 October 2012
ER -