Atlas-based image segmentation is a powerful method of segmenting an image. An atlas in this context consists of an image and its segmentation. Atlas-based segmentation works by registering the atlas-image to a subject image, and propagating the labels from the atlas-segmentation. Atlas selection has proven to be crucial for the segmentation quality. Several methods to select an atlas exist. This work compares the following atlas selection strategies: single atlas, cohort atlas, most similar atlas, average atlas, and multiple atlases. Different methods have different strengths and weaknesses. If multiple atlases are used a decision fusion algorithm is needed to fuse the propagated labels from different registrations. The VOTE and Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm for decision fusion are described here
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