Obtaining the potential number of models/atlases needed for capturing anatomic variations in population images

Ze Jin; Jayaram K. Udupa; Drew A. Torigian

doi:10.1117/12.2513073

15 March 2019 Obtaining the potential number of models/atlases needed for capturing anatomic variations in population images

Ze Jin, Jayaram K. Udupa, Drew A. Torigian

Author Affiliations +

Proceedings Volume 10949, Medical Imaging 2019: Image Processing; 109493G (2019) https://doi.org/10.1117/12.2513073
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

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 P_n(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.

Citation Download Citation

Ze Jin, Jayaram K. Udupa, and Drew A. Torigian "Obtaining the potential number of models/atlases needed for capturing anatomic variations in population images", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109493G (15 March 2019); https://doi.org/10.1117/12.2513073

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