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Proceedings Paper

Evaluation of five image registration tools for abdominal CT: pitfalls and opportunities with soft anatomy
Author(s): Christopher P. Lee; Zhoubing Xu; Ryan P. Burke; Rebeccah Baucom; Benjamin K. Poulose; Richard G. Abramson; Bennett A. Landman
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Paper Abstract

Image registration has become an essential image processing technique to compare data across time and individuals. With the successes in volumetric brain registration, general-purpose software tools are beginning to be applied to abdominal computed tomography (CT) scans. Herein, we evaluate five current tools for registering clinically acquired abdominal CT scans. Twelve abdominal organs were labeled on a set of 20 atlases to enable assessment of correspondence. The 20 atlases were pairwise registered based on only intensity information with five registration tools (affine IRTK, FNIRT, Non-Rigid IRTK, NiftyReg, and ANTs). Following the brain literature, the Dice similarity coefficient (DSC), mean surface distance, and Hausdorff distance were calculated on the registered organs individually. However, interpretation was confounded due to a significant proportion of outliers. Examining the retrospectively selected top 1 and 5 atlases for each target revealed that there was a substantive performance difference between methods. To further our understanding, we constructed majority vote segmentation with the top 5 DSC values for each organ and target. The results illustrated a median improvement of 85% in DSC between the raw results and majority vote. These experiments show that some images may be well registered to some targets using the available software tools, but there is significant room for improvement and reveals the need for innovation and research in the field of registration in abdominal CTs. If image registration is to be used for local interpretation of abdominal CT, great care must be taken to account for outliers (e.g., atlas selection in statistical fusion).

Paper Details

Date Published: 20 March 2015
PDF: 7 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94131N (20 March 2015); doi: 10.1117/12.2081045
Show Author Affiliations
Christopher P. Lee, Vanderbilt Univ. (United States)
Zhoubing Xu, Vanderbilt Univ. (United States)
Ryan P. Burke, Vanderbilt Univ. (United States)
Rebeccah Baucom, Vanderbilt Univ. (United States)
Benjamin K. Poulose, Vanderbilt Univ. (United States)
Richard G. Abramson, Vanderbilt Univ. (United States)
Bennett A. Landman, Vanderbilt Univ. (United States)


Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)

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