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Proceedings Paper

Evaluation of body-wise and organ-wise registrations for abdominal organs
Author(s): Zhoubing Xu; Sahil A. Panjwani; Christopher P. Lee; Ryan P. Burke; Rebeccah B. Baucom; Benjamin K. Poulose; Richard G. Abramson; Bennett A. Landman
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Paper Abstract

Identifying cross-sectional and longitudinal correspondence in the abdomen on computed tomography (CT) scans is necessary for quantitatively tracking change and understanding population characteristics, yet abdominal image registration is a challenging problem. The key difficulty in solving this problem is huge variations in organ dimensions and shapes across subjects. The current standard registration method uses the global or body-wise registration technique, which is based on the global topology for alignment. This method (although producing decent results) has substantial influence of outliers, thus leaving room for significant improvement. Here, we study a new image registration approach using local (organ-wise registration) by first creating organ-specific bounding boxes and then using these regions of interest (ROIs) for aligning references to target. Based on Dice Similarity Coefficient (DSC), Mean Surface Distance (MSD) and Hausdorff Distance (HD), the organ-wise approach is demonstrated to have significantly better results by minimizing the distorting effects of organ variations. This paper compares exclusively the two registration methods by providing novel quantitative and qualitative comparison data and is a subset of the more comprehensive problem of improving the multi-atlas segmentation by using organ normalization.

Paper Details

Date Published: 21 March 2016
PDF: 6 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97841O (21 March 2016); doi: 10.1117/12.2217082
Show Author Affiliations
Zhoubing Xu, Vanderbilt Univ. (United States)
Sahil A. Panjwani, Vanderbilt Univ. (United States)
Christopher P. Lee, Vanderbilt Univ. (United States)
Ryan P. Burke, Vanderbilt Univ. (United States)
Rebeccah B. 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. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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