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Gao Y, Zhu L, Cates J, et al. A Kalman Filtering Perspective for Multiatlas Segmentation. SIAM J Imaging Sci. 2015;8(2):1007-1029doi: 10.1137/130933423.
Gao, Y., Zhu, L., Cates, J., MacLeod, R. S., Bouix, S., & Tannenbaum, A. (2015). A Kalman Filtering Perspective for Multiatlas Segmentation. SIAM journal on imaging sciences, 8(2), 1007-1029. https://doi.org/10.1137/130933423
Gao, Yi, et al. "A Kalman Filtering Perspective for Multiatlas Segmentation." SIAM journal on imaging sciences vol. 8,2 (2015): 1007-1029. doi: https://doi.org/10.1137/130933423
Gao Y, Zhu L, Cates J, MacLeod RS, Bouix S, Tannenbaum A. A Kalman Filtering Perspective for Multiatlas Segmentation. SIAM J Imaging Sci. 2015;8(2):1007-1029. doi: 10.1137/130933423. Epub 2015 Apr 30. PMID: 26807162; PMCID: PMC4722821.
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SIAM J Imaging Sci. 2015;8(2):1007-1029. doi: 10.1137/130933423. Epub 2015 Apr 30.

A Kalman Filtering Perspective for Multiatlas Segmentation.

SIAM journal on imaging sciences

Yi Gao, Liangjia Zhu, Joshua Cates, Rob S MacLeod, Sylvain Bouix, Allen Tannenbaum

Affiliations

  1. Department of Biomedical Informatics and Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794.
  2. Department of Computer Science, Stony Brook University, Stony Brook, NY 11790.
  3. Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112.
  4. Department of Psychiatry, Harvard Medical School, Boston, MA 02215.
  5. Department of Computer Science and Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794.

PMID: 26807162 PMCID: PMC4722821 DOI: 10.1137/130933423

Abstract

In multiatlas segmentation, one typically registers several atlases to the novel image, and their respective segmented label images are transformed and fused to form the final segmentation. In this work, we provide a new dynamical system perspective for multiatlas segmentation, inspired by the following fact: The transformation that aligns the current atlas to the novel image can be not only computed by direct registration but also inferred from the transformation that aligns the previous atlas to the image together with the transformation between the two atlases. This process is similar to the global positioning system on a vehicle, which gets position by inquiring from the satellite and by employing the previous location and velocity-neither answer in isolation being perfect. To solve this problem, a dynamical system scheme is crucial to combine the two pieces of information; for example, a Kalman filtering scheme is used. Accordingly, in this work, a Kalman multiatlas segmentation is proposed to stabilize the global/affine registration step. The contributions of this work are twofold. First, it provides a new dynamical systematic perspective for standard independent multiatlas registrations, and it is solved by Kalman filtering. Second, with very little extra computation, it can be combined with most existing multiatlas segmentation schemes for better registration/segmentation accuracy.

Keywords: Kalman filter; dynamical systems; multiatlas segmentation; registration

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