Obtaining the potential number of models/atlases needed for capturing anatomic variations in population images
Ze Jin, Jayaram K. Udupa, Drew A. Torigian · 2019
Many medical image processing and analysis operations can benefit a great deal from prior information encoded in the form of models/atlases to capture variations over a population in form, shape, anatomic layout, and image appearance of objects. However, the fundamental question “How many models/ atlases are needed for optimally encoding prior information to address the differing body habitus factor in a given population?” has remained a difficult and open problem. We propose a method to seek an answer to this question assuming that a set Ι of images representative of the population for the body region is given. Our approach, after images in Ι are trimmed to the exact body region, is to create a partition of Ι into a specified number n of groups by optimizing the collective similarity of images in each group. We then ascertain how the overall goodness of partition Pn(Ι) varies as we change n from 1 to |Ι|. Subsequently, values of n at which there are significant changes in the goodness value are determined. These breakpoints are taken as the recommended number of groups/ models/ atlases. Our results on 284 thoracic computed tomography (CT) scans show that at least 8 groups are essential, and 15, 21, or 32 could be optimum numbers if a finer classification is needed for this population. This method may be helpful for constructing high quality models/atlases with a proper grouping of the images from a sufficiently large population and in selecting optimally the training image sets needed for each class in deep learning strategies.