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Proceedings Paper

Imposing implicit feasibility constraints on deformable image registration using a statistical generative model
Author(s): Yudi Sang; Xianglei Xing; Yingnian Wu; Dan Ruan
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Paper Abstract

Deformable registration problems are conventionally posed in a regularized optimization framework, where balance between fidelity and prescribed regularization usually needs to be manually tuned for each case. Even so, using a single weight to control regularization strength may be insufficient to reflect spatially variant tissue properties and limit registration performance. In this study, we propose to incorporate a spatially variant deformation prior into image registration framework using a statistical generative model. A generator network is trained in an unsupervised setting to maximize the likelihood of observing the moving and fixed image pairs, using an alternating back-propagation approach. The trained generative model imposes constraints on deformation and serves as an effective low dimensional deformation parametrization. During registration, optimization is performed over this learned parametrization, eliminating the need for explicit regularization and tuning. The proposed method was tested against a B-spline optimization method SimpleElastix, and an end-to-end learning method DIRNet. Experiment with synthetic images shows that our method yielded a registration error of (0.70±0.05) pixels, significantly lower than (0.86±0.12) pixels in SimpleElastix and (0.81±0.06) pixels in DIRNet. Experiment with 2D cardiac MR images demonstrates that the method completed registration with physically and physiologically more feasible deformations and the performance was close to the best of manually tuned results when evaluated with segmentation masks. The average registration time was 1.72 s, faster than 5.63 s in SimpleElastix.

Paper Details

Date Published: 10 March 2020
PDF: 8 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113132V (10 March 2020); doi: 10.1117/12.2549193
Show Author Affiliations
Yudi Sang, Univ. of California, Los Angeles (United States)
Xianglei Xing, Harbin Engineering Univ. (China)
Yingnian Wu, Univ. of California, Los Angeles (United States)
Dan Ruan, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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