Abstract
In this paper, we present a fully-automated subcortical and ventricular shape generation pipeline that acts on structural magnetic resonance images (MRIs) of the human brain. Principally, the proposed pipeline consists of three steps: (1) automated structure segmentation using the diffeomorphic multi-atlas likelihood-fusion algorithm; (2) study-specific shape template creation based on the Delaunay triangulation; (3) deformation-based shape filtering using the large deformation diffeomorphic metric mapping for surfaces. The proposed pipeline is shown to provide high accuracy, sufficient smoothness, and accurate anatomical topology. Two datasets focused upon Huntington's disease (HD) were used for evaluating the performance of the proposed pipeline. The first of these contains a total of 16 MRI scans, each with a gold standard available, on which the proposed pipeline's outputs were observed to be highly accurate and smooth when compared with the gold standard. Visual examinations and outlier analyses on the second dataset, which contains a total of 1,445 MRI scans, revealed 100% success rates for the putamen, the thalamus, the globus pallidus, the amygdala, and the lateral ventricle in both hemispheres and rates no smaller than 97% for the bilateral hippocampus and caudate. Another independent dataset, consisting of 15 atlas images and 20 testing images, was also used to quantitatively evaluate the proposed pipeline, with high accuracy having been obtained. In short, the proposed pipeline is herein demonstrated to be effective, both quantitatively and qualitatively, using a large collection of MRI scans.
Highlights
The main limitation of these shapemodeling based approaches is the lack of flexibility in relation to individual components; one may desire the ability to utilize a more accurate segmentation algorithm or a more sophisticated meshing algorithm. It is in the context of all of the above that we propose a fullyautomated subcortical and ventricular shape generation pipeline which satisfies the demand for accuracy and smoothness in four steps: (1) automatically segment the subcortical and ventricular structures of interest using the raw structural magnetic resonance images (MRIs) data acquired from a scanner; (2) create a study-specific template shape with the correct anatomical topology and sufficient surface smoothness; (3) create a triangulated mesh from each binary segmentation obtained in step (1) using the marching cubes algorithm; (4) filter and smooth the surfaces generated in step (3) in a deformation based manner
There are a total of 1,445 structural MRIs, on which we qualitatively examine the surfaces delivered by the proposed pipeline
Participants of PREIDCT-Huntington’s disease (HD) were recruited from 32 sites across the United States, Canada, Europe, and Australia and underwent longitudinal study visits consisting of a neurological motor examination, cognitive assessment, brain MRI, psychiatric and functional assessment, and blood testing for genetic and biochemical analyses
Summary
Analyzing the shape of subcortical and ventricular structures subjected to brain disorders is an area of ever growing importance, especially in the fields of neurodegenerative diseases such as Alzheimer’s disease (Qiu et al, 2009b; Wang et al, 2011; Shi et al, 2013, 2015; Tang et al, 2014, 2015b; Miller et al, 2015), Huntington’s disease (HD) (van den Bogaard et al, 2011; Younes et al, 2014; Faria et al, 2016), and Parkinson’s disease (Sterling et al, 2013; Nemmi et al, 2015) as well as various neurodevelopmental disorders (Knickmeyer et al, 2008; Rimol et al, 2010; Seymour et al, 2017). The fully automated segmentation of subcortical and ventricular structures, based on structural MRIs, is a wellestablished field of research, with a variety of highly accurate algorithms having already been developed (Barra and Boire, 2001; Khan et al, 2008; Powell et al, 2008; Patenaude et al, 2011; Chakravarty et al, 2013; Tang et al, 2015c). As for the generation of surfaces, image-based meshing is typically employed, especially when creating computer models for computational fluid dynamics and finite element analysis (Young et al, 2008; Chen et al, 2013; Chernikov et al, 2013; Foteinos and Chrisochoides, 2013; Zhang, 2013). The marching cubes algorithm takes a 3D segmentation image as its input and outputs surface data in the form of a triangulated mesh, represented using vertices and faces
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