TY - GEN
T1 - Accurate whole-brain segmentation for Alzheimer's Disease combining an adaptive statistical atlas and multi-atlas
AU - Yan, Zhennan
AU - Zhang, Shaoting
AU - Liu, Xiaofeng
AU - Metaxas, Dimitris N.
AU - Montillo, Albert
N1 - Funding Information:
Data used in the preparation of this article was obtained from the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL) funded by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) which was made available at the ADNI database ( www.loni.ucla.edu/ADNI ). The AIBL researchers contributed data but did not participate in analysis or writing of this report. AIBL researchers are listed at www.aibl.csiro.au .
PY - 2014
Y1 - 2014
N2 - Accurate segmentation of whole brain MR images including the cortex, white matter and subcortical structures is challenging due to inter-subject variability and the complex geometry of brain anatomy. However a precise solution would enable accurate, objective measurement of structure volumes for disease quantification. Our contribution is three-fold. First we construct an adaptive statistical atlas that combines structure specific relaxation and spatially varying adaptivity. Second we integrate an isotropic pairwise class-specific MRF model of label connectivity. Together these permit precise control over adaptivity, allowing many structures to be segmented simultaneously with superior accuracy. Third, we develop a framework combining the improved adaptive statistical atlas with a multi-atlas method which achieves simultaneous accurate segmentation of the cortex, ventricles, and sub-cortical structures in severely diseased brains, a feat not attained in [18]. We test the proposed method on 46 brains including 28 diseased brain with Alzheimer's and 18 healthy brains. Our proposed method yields higher accuracy than state-of-the-art approaches on both healthy and diseased brains.
AB - Accurate segmentation of whole brain MR images including the cortex, white matter and subcortical structures is challenging due to inter-subject variability and the complex geometry of brain anatomy. However a precise solution would enable accurate, objective measurement of structure volumes for disease quantification. Our contribution is three-fold. First we construct an adaptive statistical atlas that combines structure specific relaxation and spatially varying adaptivity. Second we integrate an isotropic pairwise class-specific MRF model of label connectivity. Together these permit precise control over adaptivity, allowing many structures to be segmented simultaneously with superior accuracy. Third, we develop a framework combining the improved adaptive statistical atlas with a multi-atlas method which achieves simultaneous accurate segmentation of the cortex, ventricles, and sub-cortical structures in severely diseased brains, a feat not attained in [18]. We test the proposed method on 46 brains including 28 diseased brain with Alzheimer's and 18 healthy brains. Our proposed method yields higher accuracy than state-of-the-art approaches on both healthy and diseased brains.
KW - Adaptive atlas
KW - Alzheimer's
KW - Brain segmentation
KW - MRF
KW - Multi-atlas
UR - http://www.scopus.com/inward/record.url?scp=84958544022&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84958544022&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-05530-5_7
DO - 10.1007/978-3-319-05530-5_7
M3 - Conference contribution
C2 - 31723945
AN - SCOPUS:84958544022
SN - 9783319055299
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 65
EP - 73
BT - Medical Computer Vision
PB - Springer Verlag
T2 - 3rd International MICCAI Workshop on Medical Computer Vision, MCV 2013
Y2 - 26 September 2013 through 26 September 2013
ER -