Multi-atlas learner fusion: An efficient segmentation approach for large-scale data
We build the multi-atlas learner fusion (MLF) framework for mapping weak initial segmentations to the more accurate multi-atlas segmentation. The MLF framework cuts the runtime from 36h down to 38min. We demonstrate significant increases in the reproducibility of intra-subject segmentations. We show the large-scale data model significantly improve the segmentation over the small-scale model under the MLF framework. The MLF framework has comparable performance as state-of-the-art multi-atlas segmentation algorithms without using non-local information.
multi-atlas learner fusion multi-atlas segmentation machine learning adaboost