Optimal atlas selection using image similarities in a trained regression model to predict performance

An atlas in the context of atlas-based segmentation refers to a pre-selected image with labelled anatomical regions of interest. Atlas-based segmentation is the propagation of these labels to a novel image after both images have been registered. The goal of an atlas is to be representative of an anatomical category, but in practice there exists variability in human anatomy. One solution to maintain consistent segmentation accuracies is to use multiple atlases, with a system for selecting the most appropriate atlas at the time of segmentation. This paper describes a method for selecting an atlas using a linear regression model to predict the segmentation accuracy based on image similarity measures. It goes further to present an offline method for automatically selecting a set of atlases, representative of the training set to be used during segmentation; all of this illustrated by segmentation of the heart and kidneys in 3D CT images.