Quantitative evaluation of LDDMM, FreeSurfer, and CARET for cortical surface mapping

NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore.
NeuroImage (Impact Factor: 6.36). 04/2010; 52(1):131-41. DOI: 10.1016/j.neuroimage.2010.03.085
Source: PubMed

ABSTRACT Cortical surface mapping has been widely used to compensate for individual variability of cortical shape and topology in anatomical and functional studies. While many surface mapping methods were proposed based on landmarks, curves, spherical or native cortical coordinates, few studies have extensively and quantitatively evaluated surface mapping methods across different methodologies. In this study we compared five cortical surface mapping algorithms, including large deformation diffeomorphic metric mapping (LDDMM) for curves (LDDMM-curve), for surfaces (LDDMM-surface), multi-manifold LDDMM (MM-LDDMM), FreeSurfer, and CARET, using 40 MRI scans and 10 simulated datasets. We computed curve variation errors and surface alignment consistency for assessing the mapping accuracy of local cortical features (e.g., gyral/sulcal curves and sulcal regions) and the curvature correlation for measuring the mapping accuracy in terms of overall cortical shape. In addition, the simulated datasets facilitated the investigation of mapping error distribution over the cortical surface when the MM-LDDMM, FreeSurfer, and CARET mapping algorithms were applied. Our results revealed that the LDDMM-curve, MM-LDDMM, and CARET approaches best aligned the local curve features with their own curves. The MM-LDDMM approach was also found to be the best in aligning the local regions and cortical folding patterns (e.g., curvature) as compared to the other mapping approaches. The simulation experiment showed that the MM-LDDMM mapping yielded less local and global deformation errors than the CARET and FreeSurfer mappings.

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    • "Thus, surface-based approaches have recently received great attention and been applied in brain morphometry for exploring abnormalities [4] [28] [31]. Many related comparisons and surveys have also been presented, as in [23] [29] [38]. "
    Computer Vision and Image Understanding 09/2013; 117(9):1107-1118. · 1.36 Impact Factor
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    • "Thus, surface-based approaches have recently received great attention and been applied in brain morphometry for exploring abnormalities [4] [28] [31]. Many related comparisons and surveys have also been presented, as in [23] [29] [38]. "
    X Chen · H He · G Zou · X Zhang · X Gu · J Hua
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    ABSTRACT: This paper presents an improved Euclidean Ricci flow method for spherical parameterization. We subsequently invent a scale space processing built upon Ricci energy to extract robust surface features for accurate surface registration. Since our method is based on the proposed Euclidean Ricci flow, it inherits the properties of Ricci flow such as conformality, robustness and intrinsicalness, facilitating efficient and effective surface mapping. Compared with other surface registration methods using curvature or sulci pattern, our method demonstrates a significant improvement for surface registration. In addition, Ricci energy can capture local differences for surface analysis as shown in the experiments and applications.
    Computer Vision and Image Understanding 09/2013; 117(9):1107-1118. DOI:10.1016/j.cviu.2013.02.010 · 1.36 Impact Factor
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    • "This approach has been shown to be more robust in surface registration as compared to fully automated methods of registration (Pantazis et al. 2010). Given our a priori ROI driven hypothesis and neural systems-based approach, we think that this study benefits the most from the landmark-based technique as the anatomical regions adjacent to the landmarks are optimally registered (Zhong et al. 2010). The registration method uses a deformable, spherical registration that is constrained by the landmarks. "
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