Article

Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy

Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Medical image analysis (Impact Factor: 3.65). 11/2008; 12(5):603-15. DOI: 10.1016/j.media.2008.06.005
Source: PubMed

ABSTRACT

In non-rigid registration, the tradeoff between warp regularization and image fidelity is typically determined empirically. In atlas-based segmentation, this leads to a probabilistic atlas of arbitrary sharpness: weak regularization results in well-aligned training images and a sharp atlas; strong regularization yields a "blurry" atlas. In this paper, we employ a generative model for the joint registration and segmentation of images. The atlas construction process arises naturally as estimation of the model parameters. This framework allows the computation of unbiased atlases from manually labeled data at various degrees of "sharpness", as well as the joint registration and segmentation of a novel brain in a consistent manner. We study the effects of the tradeoff of atlas sharpness and warp smoothness in the context of cortical surface parcellation. This is an important question because of the increasingly availability of atlases in public databases, and the development of registration algorithms separate from the atlas construction process. We find that the optimal segmentation (parcellation) corresponds to a unique balance of atlas sharpness and warp regularization, yielding statistically significant improvements over the FreeSurfer parcellation algorithm. Furthermore, we conclude that one can simply use a single atlas computed at an optimal sharpness for the registration-segmentation of a new subject with a pre-determined, fixed, optimal warp constraint. The optimal atlas sharpness and warp smoothness can be determined by probing the segmentation performance on available training data. Our experiments also suggest that segmentation accuracy is tolerant up to a small mismatch between atlas sharpness and warp smoothness.

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Available from: Mert R Sabuncu, Jun 19, 2014
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    • "Quantitative data for the deep brain grey matter structures of interest and corpus callosum were then extracted from the resulting set for statistical analyses. For cortical analysis, the grey matter–white matter interface of each hemisphere was segmented, tessellated, corrected for topological errors (Fischl et al., 2001; Segonne et al., 2007; Yeo et al., 2008) and inflated in order to unfold the cortical surface. Each individual hemispheric surface was then registered to a surface-based template (Fischl et al., 1999a) using its sulco-gyral pattern to allow intersubjects analysis. "

    Full-text · Dataset · Jan 2016
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    • "Quantitative data for the deep brain grey matter structures of interest and corpus callosum were then extracted from the resulting set for statistical analyses. For cortical analysis, the grey matter–white matter interface of each hemisphere was segmented, tessellated, corrected for topological errors (Fischl et al., 2001; Segonne et al., 2007; Yeo et al., 2008) and inflated in order to unfold the cortical surface. Each individual hemispheric surface was then registered to a surface-based template (Fischl et al., 1999a) using its sulco-gyral pattern to allow intersubjects analysis. "
    Dataset: document

    Full-text · Dataset · Oct 2015
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    • "Spatial normalisation of the training atlases can be achieved with different registration algorithms. Because registration methods are a trade-off between warp regularisation and the fidelity term, probabilistic atlases possess arbitrary sharpness: weak regularisation leads to a sharp atlas, whereas strong regularisation yields a blurry atlas [14]. Given a smoothness parameter that controls the registration, an iterative atlas generation scheme is usually employed [11, 15, 16]. "
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