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Multi-atlas Segmentation with Particle-based Group-wise image Registration

Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Publication Date:
Proc Soc Photo Opt Instrum Eng
Volume Number:
Proc Soc Photo Opt Instrum Eng. 2014 Mar 21;9034:903447.
PubMed ID:
b-spline deformation, Groupwise Registration, multi-atlas segmentation, particle system, registration, segmentation
Appears in Collections:
P01 DA022446/DA/NIDA NIH HHS/United States
P30 HD003110/HD/NICHD NIH HHS/United States
R41 NS059095/NS/NINDS NIH HHS/United States
U01 AA020023/AA/NIAAA NIH HHS/United States
U24 AA020022/AA/NIAAA NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
U54 HD079124/HD/NICHD NIH HHS/United States
Generated Citation:
Lee J., Lyu I., Styner M. Multi-atlas Segmentation with Particle-based Group-wise image Registration. Proc Soc Photo Opt Instrum Eng. 2014 Mar 21;9034:903447. PMID: 25075158. PMCID: PMC4112129.
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We propose a novel multi-atlas segmentation method that employs a group-wise image registration method for the brain segmentation on rodent magnetic resonance (MR) images. The core element of the proposed segmentation is the use of a particle-guided image registration method that extends the concept of particle correspondence into the volumetric image domain. The registration method performs a group-wise image registration that simultaneously registers a set of images toward the space defined by the average of particles. The particle-guided image registration method is robust with low signal-to-noise ratio images as well as differing sizes and shapes observed in the developing rodent brain. Also, the use of an implicit common reference frame can prevent potential bias induced by the use of a single template in the segmentation process. We show that the use of a particle guided-image registration method can be naturally extended to a novel multi-atlas segmentation method and improves the registration method to explicitly use the provided template labels as an additional constraint. In the experiment, we show that our segmentation algorithm provides more accuracy with multi-atlas label fusion and stability against pair-wise image registration. The comparison with previous group-wise registration method is provided as well.

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