Model-Guided Segmentation of Corpus Callosum in MR Images
Abstract
Magnetic resonance imaging (MRI) of the brain, followed by automated segmentation of the corpus callosum (CC) in midsagittal sections has important applications in neurology and neurocognitive research since the size and shape of the CC are shown to be correlated to sex, age, neurodegenerative diseases and various lateralized behavior in man. Moreover, whole head, multispectral 3D MRI recordings enable voxel-based tissue classification and estimation of total brain volumes, in addition to CC morphometric parameters. We propose a new algorithm that uses both multispectral MRI measurements (intensity values) and prior information about shape (CC template) to segment CC in midsagittal slices with very little user interaction. The algorithm has been successfully tested on a sample of 10 subjects scanned with multispectral 3D MRI, collected for a study of dyslexia. We conclude that the proposed method for CC segmentation is promising for clinical use when multispectral MR images are recorded.
Cite
Text
Lundervold et al. "Model-Guided Segmentation of Corpus Callosum in MR Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999. doi:10.1109/CVPR.1999.786944Markdown
[Lundervold et al. "Model-Guided Segmentation of Corpus Callosum in MR Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999.](https://mlanthology.org/cvpr/1999/lundervold1999cvpr-model/) doi:10.1109/CVPR.1999.786944BibTeX
@inproceedings{lundervold1999cvpr-model,
title = {{Model-Guided Segmentation of Corpus Callosum in MR Images}},
author = {Lundervold, Arvid and Taxt, Torfinn and Duta, Nicolae and Jain, Anil K.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {1999},
pages = {1231-1237},
doi = {10.1109/CVPR.1999.786944},
url = {https://mlanthology.org/cvpr/1999/lundervold1999cvpr-model/}
}