GPU-Accelerated, Gradient-Free MI Deformable Registration for Atlas-Based MR Brain Image Segmentation
Abstract
Brain structure segmentation is an important task in many neuroscience and clinical applications. In this paper, we introduce a novel MI-based dense deformable registration method and apply it to the automatic segmentation of detailed brain structures. Together with a multiple atlas fusion strategy, very accurate segmentation results were obtained, as compared with other reported methods in the literature. To make multi-atlas segmentation computationally feasible, we also propose to take advantage of the recent advancements in GPU technology and introduce a GPU-based implementation of the proposed registration method. With GPU acceleration it takes less than 8 minutes to compile a multi-atlas segmentation for each subject even with as many as 17 atlases, which demonstrates that the use of GPUs can greatly facilitate the application of such atlas-based segmentation methods in practice.
Cite
Text
Han et al. "GPU-Accelerated, Gradient-Free MI Deformable Registration for Atlas-Based MR Brain Image Segmentation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2009. doi:10.1109/CVPRW.2009.5204043Markdown
[Han et al. "GPU-Accelerated, Gradient-Free MI Deformable Registration for Atlas-Based MR Brain Image Segmentation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2009.](https://mlanthology.org/cvprw/2009/han2009cvprw-gpuaccelerated/) doi:10.1109/CVPRW.2009.5204043BibTeX
@inproceedings{han2009cvprw-gpuaccelerated,
title = {{GPU-Accelerated, Gradient-Free MI Deformable Registration for Atlas-Based MR Brain Image Segmentation}},
author = {Han, Xiao and Hibbard, Lyndon S. and Willcut, Virgil},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2009},
pages = {141-148},
doi = {10.1109/CVPRW.2009.5204043},
url = {https://mlanthology.org/cvprw/2009/han2009cvprw-gpuaccelerated/}
}