Learning to Rank Atlases for Multiple-Atlas Segmentation

@article{Sanroma2014LearningTR,
  title={Learning to Rank Atlases for Multiple-Atlas Segmentation},
  author={Gerard Sanroma and Guorong Wu and Yaozong Gao and Dinggang Shen},
  journal={IEEE Transactions on Medical Imaging},
  year={2014},
  volume={33},
  pages={1939-1953}
}
Recently, multiple-atlas segmentation (MAS) has achieved a great success in the medical imaging area. The key assumption is that multiple atlases have greater chances of correctly labeling a target image than a single atlas. However, the problem of atlas selection still remains unexplored. Traditionally, image similarity is used to select a set of atlases. Unfortunately, this heuristic criterion is not necessarily related to the final segmentation performance. To solve this seemingly simple but… CONTINUE READING

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