Measuring Cortical Thickness Using an Image Domain Local Surface Model and Topology Preserving Segmentation
Abstract
We present a measure of gray matter (GM) thickness based on local surface models in the image domain. Thickness is measured by integrating GM probability maps along the white matter (WM) surface normal direction. The method is simple to implement and allows statistical tests to be performed in the gray matter volume. A novel topology preserving segmentation method is introduced that is able to accurately recover GM in deep sulci. We apply this methodology to a longitudinal study of gray matter atrophy in a patient cohort diagnosed with frontotemporal dementia (FTD) spectrum disorders. Following image-based normalization of GM thickness maps, results show significant reduction in cortical thickness in several Brodmann areas spanning temporal, parietal and frontal lobes across subjects.
Cite
Text
Das et al. "Measuring Cortical Thickness Using an Image Domain Local Surface Model and Topology Preserving Segmentation." IEEE/CVF International Conference on Computer Vision, 2007. doi:10.1109/ICCV.2007.4409136Markdown
[Das et al. "Measuring Cortical Thickness Using an Image Domain Local Surface Model and Topology Preserving Segmentation." IEEE/CVF International Conference on Computer Vision, 2007.](https://mlanthology.org/iccv/2007/das2007iccv-measuring/) doi:10.1109/ICCV.2007.4409136BibTeX
@inproceedings{das2007iccv-measuring,
title = {{Measuring Cortical Thickness Using an Image Domain Local Surface Model and Topology Preserving Segmentation}},
author = {Das, Sandhitsu R. and Avants, Brian B. and Grossman, Murray and Gee, James C.},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {2007},
pages = {1-8},
doi = {10.1109/ICCV.2007.4409136},
url = {https://mlanthology.org/iccv/2007/das2007iccv-measuring/}
}