Image segmentation

A method of selecting a plurality of M atlases from among a larger group of N candidate atlases to form a multi-atlas data set to be used for computer automated segmentation of novel image data sets to mark objects of interest therein, the method comprising:

• (a) providing a computer with a set of candidate atlases, each candidate atlas containing a reference image data set and segmentation data which marks an object of interest within the reference image data set;
• (b) providing the computer with a plurality of training image data sets, each containing a training image data set and segmentation data which marks an object of interest within the training image data set;
• (c) segmenting in the computer each of the training image data sets against each of the candidate atlases such that for each candidate atlas in turn the following is carried out:
• (i) registering the candidate atlas with each of the training image data sets;
• (ii) segmenting each of the training image data sets with the candidate atlas;
• (iii) computing an overlap between each training image data set and the candidate atlas;
• (iv) computing a value of the similarity measure for each of the registrations;
• (v) obtaining a set of regression parameters by performing a regression with the similarity measure being the independent variable and the overlap being the dependent variable;
• (d) selecting in the computer a subset of M atlases from among all the N candidate atlases to form the multi-atlas data set, the M atlases being those atlases determined to collectively provide the highest aggregate overlap over all the training data image sets.