ML Anthology

Regression-Based Label Fusion for Multi-Atlas Segmentation

Hongzhi Wang, Jung Wook Suh, Sandhitsu R. Das, John Pluta, Murat Altinay, Paul A. Yushkevich
CVPR 2011 pp. 1113-1120
doi:10.1109/CVPR.2011.5995382 /cvpr/2011/wang2011cvpr-regression/

Abstract

Automatic segmentation using multi-atlas label fusion has been widely applied in medical image analysis. To simplify the label fusion problem, most methods implicitly make a strong assumption that the segmentation errors produced by different atlases are uncorrelated. We show that violating this assumption significantly reduces the efficiency of multi-atlas segmentation. To address this problem, we propose a regression-based approach for label fusion. Our experiments on segmenting the hippocampus in magnetic resonance images (MRI) show significant improvement over previous label fusion techniques.

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Cite

Text

Wang et al. "Regression-Based Label Fusion for Multi-Atlas Segmentation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011. doi:10.1109/CVPR.2011.5995382

Markdown

[Wang et al. "Regression-Based Label Fusion for Multi-Atlas Segmentation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011.](https://mlanthology.org/cvpr/2011/wang2011cvpr-regression/) doi:10.1109/CVPR.2011.5995382

BibTeX

@inproceedings{wang2011cvpr-regression,
  title     = {{Regression-Based Label Fusion for Multi-Atlas Segmentation}},
  author    = {Wang, Hongzhi and Suh, Jung Wook and Das, Sandhitsu R. and Pluta, John and Altinay, Murat and Yushkevich, Paul A.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2011},
  pages     = {1113-1120},
  doi       = {10.1109/CVPR.2011.5995382},
  url       = {https://mlanthology.org/cvpr/2011/wang2011cvpr-regression/}
}