Abstract
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.
Keywords
- Cortical Thickness
- Average Cortical Thickness
- Stable Mild Cognitive Impairment
- Cortical Thickness Change
- Cortical Surface Reconstruction
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Wang, L., Shi, F., Li, G., Shen, D. (2012). 4D Segmentation of Longitudinal Brain MR Images with Consistent Cortical Thickness Measurement. In: Durrleman, S., Fletcher, T., Gerig, G., Niethammer, M. (eds) Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data. STIA 2012. Lecture Notes in Computer Science, vol 7570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33555-6_6
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DOI: https://doi.org/10.1007/978-3-642-33555-6_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33554-9
Online ISBN: 978-3-642-33555-6
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