TY - GEN
T1 - 4D segmentation of longitudinal brain MR images with consistent cortical thickness measurement
AU - Wang, Li
AU - Shi, Feng
AU - Li, Gang
AU - Shen, Dinggang
PY - 2012
Y1 - 2012
N2 - Accurate segmentation of the brain MR images plays an important role in investigation of neurodegenerative changes in the cerebral cortex. However, most of the previous algorithms were proposed for segmentation of 3D images and few studies have taken the temporal consistency of cortical-thickness changes into account during the longitudinal studies. In this paper, we propose a 4D segmentation framework for the adult brain MR images with consistent longitudinal cortical thickness changes. Specifically, we utilize local intensity information to address the intensity inhomogeneity, spatial cortical thickness constraint to maintain the cortical thickness within a reasonable range, and temporal cortical thickness constraint to ensure the cortical thickness at the current time-point to be temporally consistent with thicknesses in the neighboring time-points. The proposed method has been tested on BLSA dataset and ADNI dataset. Both qualitative and quantitative experimental results demonstrate the accuracy and consistency of the proposed method, in comparison to other state-of-the-art 4D segmentation methods.
AB - Accurate segmentation of the brain MR images plays an important role in investigation of neurodegenerative changes in the cerebral cortex. However, most of the previous algorithms were proposed for segmentation of 3D images and few studies have taken the temporal consistency of cortical-thickness changes into account during the longitudinal studies. In this paper, we propose a 4D segmentation framework for the adult brain MR images with consistent longitudinal cortical thickness changes. Specifically, we utilize local intensity information to address the intensity inhomogeneity, spatial cortical thickness constraint to maintain the cortical thickness within a reasonable range, and temporal cortical thickness constraint to ensure the cortical thickness at the current time-point to be temporally consistent with thicknesses in the neighboring time-points. The proposed method has been tested on BLSA dataset and ADNI dataset. Both qualitative and quantitative experimental results demonstrate the accuracy and consistency of the proposed method, in comparison to other state-of-the-art 4D segmentation methods.
UR - https://www.scopus.com/pages/publications/84867849355
U2 - 10.1007/978-3-642-33555-6_6
DO - 10.1007/978-3-642-33555-6_6
M3 - Conference contribution
AN - SCOPUS:84867849355
SN - 9783642335549
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 63
EP - 75
BT - Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data - Second International Workshop, STIA 2012, Held in Conjunction with MICCAI 2012, Proceedings
T2 - 2nd International Workshop on Spatiotemporal Image Analysis for Longitudinal and Time-Series Image Data, STIA 2012, Held in Conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
Y2 - 1 October 2012 through 1 October 2012
ER -