TY - JOUR
T1 - CLASSIC
T2 - 19th International Conference on Information Processing in Medical Imaging, IPMI 2005
AU - Xue, Zhong
AU - Shen, Dinggang
AU - Davatzikos, Christos
N1 - Funding Information:
This work was supported in part by grants R01AG14971, N01-AG32124-09. We thank Dr. Dzung Pham and Dr. Jerry Prince from Johns Hopkins University for providing the software of the FANTASM algorithm and Dr. Susan Resnick from NIH for access to the BLSA data.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2005
Y1 - 2005
N2 - This paper proposes a temporally-consistent and spatially-adaptive longitudinal MR brain image segmentation algorithm, referred to as CLASSIC, which aims at obtaining accurate measurements of rates of change of regional and global brain volumes from serial MR images. The algorithm incorporates image-adaptive clustering, spatiotemporal smoothness constraints, and image warping to jointly segment a series of 3-D MR brain images of the same subject that might be undergoing changes due to development, aging or disease. Morphological changes, such as growth or atrophy, are also estimated as part of the algorithm. Experimental results on simulated and real longitudinal MR brain images show both segmentation accuracy and longitudinal consistency.
AB - This paper proposes a temporally-consistent and spatially-adaptive longitudinal MR brain image segmentation algorithm, referred to as CLASSIC, which aims at obtaining accurate measurements of rates of change of regional and global brain volumes from serial MR images. The algorithm incorporates image-adaptive clustering, spatiotemporal smoothness constraints, and image warping to jointly segment a series of 3-D MR brain images of the same subject that might be undergoing changes due to development, aging or disease. Morphological changes, such as growth or atrophy, are also estimated as part of the algorithm. Experimental results on simulated and real longitudinal MR brain images show both segmentation accuracy and longitudinal consistency.
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U2 - 10.1007/11505730_9
DO - 10.1007/11505730_9
M3 - Conference article
C2 - 17354688
AN - SCOPUS:34047228202
SN - 0302-9743
VL - 3565
SP - 101
EP - 113
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Y2 - 10 July 2005 through 15 July 2005
ER -