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. Author manuscript; available in PMC: 2016 Dec 1.
Published in final edited form as: Med Image Anal. 2015 Aug 28;26(1):82–91. doi: 10.1016/j.media.2015.08.010

Figure 1.

Figure 1

Flowchart demonstrating the multi-atlas learner fusion (MLF) framework. A large collection of training images are processed offline using a typical multi-atlas segmentation pipeline. The dimensionality of the training images is then reduced, and learners are constructed to map a weak initial estimate to the multi-atlas segmentation. Finally, for a new testing image, the image needs to be projected into the low-dimensional space and the locally appropriate learners can be fused to efficiently and accurately estimate the final segmentation.