Optimal weights for local multi-atlas fusion using supervised learning and dynamic information (SuperDyn): Validation on hippocampus segmentation

@article{Khan2011OptimalWF,
  title={Optimal weights for local multi-atlas fusion using supervised learning and dynamic information (SuperDyn): Validation on hippocampus segmentation},
  author={Ali R. Khan and Nicolas Cherbuin and Wei Wen and Kaarin Anstey and Perminder S. Sachdev and Mirza Faisal Beg},
  journal={NeuroImage},
  year={2011},
  volume={56},
  pages={126-139}
}
We developed a novel method for spatially-local selection of atlas-weights in multi-atlas segmentation that combines supervised learning on a training set and dynamic information in the form of local registration accuracy estimates (SuperDyn). Supervised learning was applied using a jackknife learning approach and the methods were evaluated using leave-N-out cross-validation. We applied our segmentation method to hippocampal segmentation in 1.5T and 3T MRI from two datasets: 69 healthy middle… CONTINUE READING
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