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Obtaining the potential number of models/atlases needed for capturing anatomic variations in population images
Author(s): Ze Jin; Jayaram K. Udupa; Drew A. Torigian
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Paper 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 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.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109493G (15 March 2019); doi: 10.1117/12.2513073
Show Author Affiliations
Ze Jin, Univ. of Pennsylvania (United States)
Jayaram K. Udupa, Univ. of Pennsylvania (United States)
Drew A. Torigian, Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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