TY - JOUR
T1 - Brain tissue segmentation based on DTI data
AU - Liu, Tianming
AU - Li, Hai
AU - Wong, Kelvin
AU - Tarokh, Ashley
AU - Guo, Lei
AU - Wong, Stephen T.C.
N1 - Funding Information:
This research was funded by a research grant to STCW by Harvard Center for Neurodegeneration and Repair, Harvard Medical School. Parts of public DTI and SPGR datasets from NAMIC were provided by the Laboratory of Neuroscience, Department of Psychiatry, Boston VA Healthcare System and Harvard Medical School, which is supported by the following grants: NIMH R01 MH50740 (Shenton), NIH K05 MH01110 (Shenton), NIMH R01 MH52807 (McCarley), NIMH R01 MH40799 (McCarley), VA Merit Awards (Shenton; McCarley), and VA Research Enhancement Award Program (REAP: McCarley). We want to express our thanks to Dr. Susumu Mori for sharing the DTIStudio software and DTI datasets, to STAPLE authors and ITK for sharing the STAPLE filter, and to FSL developers.
PY - 2007/10/15
Y1 - 2007/10/15
N2 - We present a method for automated brain tissue segmentation based on the multi-channel fusion of diffusion tensor imaging (DTI) data. The method is motivated by the evidence that independent tissue segmentation based on DTI parametric images provides complementary information of tissue contrast to the tissue segmentation based on structural MRI data. This has important applications in defining accurate tissue maps when fusing structural data with diffusion data. In the absence of structural data, tissue segmentation based on DTI data provides an alternative means to obtain brain tissue segmentation. Our approach to the tissue segmentation based on DTI data is to classify the brain into two compartments by utilizing the tissue contrast existing in a single channel. Specifically, because the apparent diffusion coefficient (ADC) values in the cerebrospinal fluid (CSF) are more than twice that of gray matter (GM) and white matter (WM), we use ADC images to distinguish CSF and non-CSF tissues. Additionally, fractional anisotropy (FA) images are used to separate WM from non-WM tissues, as highly directional white matter structures have much larger fractional anisotropy values. Moreover, other channels to separate tissue are explored, such as eigenvalues of the tensor, relative anisotropy (RA), and volume ratio (VR). We developed an approach based on the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm that combines these two-class maps to obtain a complete tissue segmentation map of CSF, GM, and WM. Evaluations are provided to demonstrate the performance of our approach. Experimental results of applying this approach to brain tissue segmentation and deformable registration of DTI data and spoiled gradient-echo (SPGR) data are also provided.
AB - We present a method for automated brain tissue segmentation based on the multi-channel fusion of diffusion tensor imaging (DTI) data. The method is motivated by the evidence that independent tissue segmentation based on DTI parametric images provides complementary information of tissue contrast to the tissue segmentation based on structural MRI data. This has important applications in defining accurate tissue maps when fusing structural data with diffusion data. In the absence of structural data, tissue segmentation based on DTI data provides an alternative means to obtain brain tissue segmentation. Our approach to the tissue segmentation based on DTI data is to classify the brain into two compartments by utilizing the tissue contrast existing in a single channel. Specifically, because the apparent diffusion coefficient (ADC) values in the cerebrospinal fluid (CSF) are more than twice that of gray matter (GM) and white matter (WM), we use ADC images to distinguish CSF and non-CSF tissues. Additionally, fractional anisotropy (FA) images are used to separate WM from non-WM tissues, as highly directional white matter structures have much larger fractional anisotropy values. Moreover, other channels to separate tissue are explored, such as eigenvalues of the tensor, relative anisotropy (RA), and volume ratio (VR). We developed an approach based on the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm that combines these two-class maps to obtain a complete tissue segmentation map of CSF, GM, and WM. Evaluations are provided to demonstrate the performance of our approach. Experimental results of applying this approach to brain tissue segmentation and deformable registration of DTI data and spoiled gradient-echo (SPGR) data are also provided.
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U2 - 10.1016/j.neuroimage.2007.07.002
DO - 10.1016/j.neuroimage.2007.07.002
M3 - Article
C2 - 17804258
AN - SCOPUS:34548759051
SN - 1053-8119
VL - 38
SP - 114
EP - 123
JO - NeuroImage
JF - NeuroImage
IS - 1
ER -