An analysis of methods for the selection of atlases for use in medical image segmentation

The use of atlases has been shown to be a robust method for segmentation of medical images. In this paper we explore different methods of selection of atlases for the segmentation of the quadriceps muscles in magnetic resonance (MR) images, although the results are pertinent for a wide range of applications. The experiments were performed using 103 images from the Osteoarthritis Initiative (OAI). The images were randomly split into a training set consisting of 50 images and a testing set of 53 images. Three different atlas selection methods were systematically compared. First, a set of readers was assigned the task of selecting atlases from a training population of images, which were selected to be representative subgroups of the total population. Second, the same readers were instructed to select atlases from a subset of the training data which was stratified based on population modes. Finally, every image in the training set was employed as an atlas, with no input from the readers, and the atlas which had the best initial registration, judged by an appropriate registration metric, was used in the final segmentation procedure. The segmentation results were quantified using the Zijdenbos similarity index (ZSI). The results show that over all readers the agreement of the segmentation algorithm decreased from 0.76 to 0.74 when using population modes to assist in atlas selection. The use of every image in the training set as an atlas outperformed both manual atlas selection methods, achieving a ZSI of 0.82.