Performing label-fusion-based segmentation using multiple automatically generated templates.

@article{Chakravarty2013PerformingLS,
  title={Performing label-fusion-based segmentation using multiple automatically generated templates.},
  author={M. Mallar Chakravarty and Patrick E. Steadman and Matthijs C van Eede and Rebecca D. Calcott and Victoria Gu and Philip Shaw and Armin Raznahan and D. Louis Collins and Jason P. Lerch},
  journal={Human brain mapping},
  year={2013},
  volume={34 10},
  pages={2635-54}
}
Classically, model-based segmentation procedures match magnetic resonance imaging (MRI) volumes to an expertly labeled atlas using nonlinear registration. The accuracy of these techniques are limited due to atlas biases, misregistration, and resampling error. Multi-atlas-based approaches are used as a remedy and involve matching each subject to a number of manually labeled templates. This approach yields numerous independent segmentations that are fused using a voxel-by-voxel label-voting… CONTINUE READING
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