Elastic Matching Using a Deformation Sphere
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Model Library for Deformable Model-Based Segmentation of 3-D Brain MR-Images
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Multi-contrast deep nuclei segmentation using a probabilistic atlas
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
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Multi-atlas segmentation has been proved to perform well in segmenting sub-cortical structures from images. In this work, we study different components of multi-atlas segmentation and propose new techniques to improve the segmentation accuracy. We found that the use of gradient information in addition to standard normalised mutual information increases the registration accuracy. We also studied different techniques to select atlases in the multi-atlas segmentation. In addition, the expectation maximisation algorithm was used to combinemulti-atlas and intensity model information. The average similarity index obtained for six subcortical structures was 0.84.