Quantitative evaluation of LDDMM, FreeSurfer, and CARET for cortical surface mapping.
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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ABSTRACT: Neuroanatomic phenotypes are often assessed using volumetric analysis. Although powerful and versatile, this approach is limited in that it is unable to quantify changes in shape, to describe how regions are interrelated, or to determine whether changes in size are global or local. Statistical shape analysis using coordinate data from biologically relevant landmarks is the preferred method for testing these aspects of phenotype. To date, approximately fifty landmarks have been used to study brain shape. Of the studies that have used landmark-based statistical shape analysis of the brain, most have not published protocols for landmark identification or the results of reliability studies on these landmarks. The primary aims of this study were two-fold: (1) to collaboratively develop detailed data collection protocols for a set of brain landmarks, and (2) to complete an intra- and inter-observer validation study of the set of landmarks. Detailed protocols were developed for 29 cortical and subcortical landmarks using a sample of 10 boys aged 12 years old. Average intra-observer error for the final set of landmarks was 1.9 mm with a range of 0.72 mm-5.6 mm. Average inter-observer error was 1.1 mm with a range of 0.40 mm-3.4 mm. This study successfully establishes landmark protocols with a minimal level of error that can be used by other researchers in the assessment of neuroanatomic phenotypes.PLoS ONE 01/2014; 9(1):e86005. · 3.53 Impact Factor
Conference Paper: Hyperbolic Harmonic Mapping for Constrained Brain Surface Registration[Show abstract] [Hide abstract]
ABSTRACT: Automatic computation of surface correspondence via harmonic map is an active research field in computer vision, computer graphics and computational geometry. It may help document and understand physical and biological phenomena and also has broad applications in biometrics, medical imaging and motion capture. Although numerous studies have been devoted to harmonic map research, limited progress has been made to compute a diffeomorphic harmonic map on general topology surfaces with landmark constraints. This work conquer this problem by changing the Riemannian metric on the target surface to a hyperbolic metric, so that the harmonic mapping is guaranteed to be a diffeomorphism under landmark constraints. The computational algorithms are based on the Ricci flow method and the method is general and robust. We apply our algorithm to study constrained human brain surface registration problem. Experimental results demonstrate that, by changing the Riemannian metric, the registrations are always dif- feomorphic, and achieve relative high performance when evaluated with some popular cortical surface registration evaluation standards.Computer Visiion and Pattern Recognition ( CVPR 2013), Portland, Oregon; 06/2013
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ABSTRACT: This article describes a novel approach to identify autism spectrum disorder (ASD) utilizing regional and interregional morphological patterns extracted from structural magnetic resonance images. Two types of features are extracted to characterize the morphological patterns: (1) Regional features, which includes the cortical thickness, volumes of cortical gray matter, and cortical-associated white matter regions, and several subcortical structures extracted from different regions-of-interest (ROIs); (2) Interregional features, which convey the morphological change pattern between pairs of ROIs. We demonstrate that the integration of regional and interregional features via multi-kernel learning technique can significantly improve the classification performance of ASD, compared with using either regional or interregional features alone. Specifically, the proposed framework achieves an accuracy of 96.27% and an area of 0.9952 under the receiver operating characteristic curve, indicating excellent diagnostic power and generalizability. The best performance is achieved when both feature types are weighted approximately equal, indicating complementary between these two feature types. Regions that contributed the most to classification are in line with those reported in the previous studies, particularly the subcortical structures that are highly associated with human emotional modulation and memory formation. The autistic brains demonstrate a significant rightward asymmetry pattern particularly in the auditory language areas. These findings are in agreement with the fact that ASD is a behavioral- and language-related neurodevelopmental disorder. By concurrent consideration of both regional and interregional features, the current work presents an effective means for better characterization of neurobiological underpinnings of ASD that facilitates its identification from typically developing children.Human Brain Mapping 01/2013; 35(7). · 6.92 Impact Factor