Atlas-Based Segmentation of Brain Magnetic Resonance Imaging Using Random Walks
Abstract
The segmentation of brain magnetic resonance imaging is a difficult task, essential to several applications in neuroscience. Atlas-based methods are often employed for this task since they provide prior information in the form of labels, without the manual intervention of a trained technician. In this paper, we present a novel and efficient atlas-based segmentation method based on random walks. Unlike most atlas-based approaches, our method combines the registration and label propagation steps in a single efficient framework. Moreover, this method does not depend on a specific deformation model, making it more robust to complex transformations not captured by such models. Experiments on benchmark brain MRI data show the usefulness and efficiency of our method.
Cite
Text
Morin et al. "Atlas-Based Segmentation of Brain Magnetic Resonance Imaging Using Random Walks." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2012. doi:10.1109/CVPRW.2012.6239246Markdown
[Morin et al. "Atlas-Based Segmentation of Brain Magnetic Resonance Imaging Using Random Walks." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2012.](https://mlanthology.org/cvprw/2012/morin2012cvprw-atlasbased/) doi:10.1109/CVPRW.2012.6239246BibTeX
@inproceedings{morin2012cvprw-atlasbased,
title = {{Atlas-Based Segmentation of Brain Magnetic Resonance Imaging Using Random Walks}},
author = {Morin, Jean-Philippe and Desrosiers, Christian and Duong, Luc},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2012},
pages = {44-49},
doi = {10.1109/CVPRW.2012.6239246},
url = {https://mlanthology.org/cvprw/2012/morin2012cvprw-atlasbased/}
}