Dataset Information

Skeletal Shape Correspondence Through Entropy.

ABSTRACT: We present a novel approach for improving the shape statistics of medical image objects by generating correspondence of skeletal points. Each object's interior is modeled by an s-rep, i.e., by a sampled, folded, two-sided skeletal sheet with spoke vectors proceeding from the skeletal sheet to the boundary. The skeleton is divided into three parts: the up side, the down side, and the fold curve. The spokes on each part are treated separately and, using spoke interpolation, are shifted along that skeleton in each training sample so as to tighten the probability distribution on those spokes' geometric properties while sampling the object interior regularly. As with the surface/boundary-based correspondence method of Cates et al., entropy is used to measure both the probability distribution tightness and the sampling regularity, here of the spokes' geometric properties. Evaluation on synthetic and real world lateral ventricle and hippocampus data sets demonstrate improvement in the performance of statistics using the resulting probability distributions. This improvement is greater than that achieved by an entropy-based correspondence method on the boundary points.

SUBMITTER: Tu L

PROVIDER: S-EPMC5943061 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Skeletal Shape Correspondence Through Entropy.

Tu Liyun L Styner Martin M Vicory Jared J Elhabian Shireen S Wang Rui R Hong Junpyo J Paniagua Beatriz B Prieto Juan C JC Yang Dan D Whitaker Ross R Pizer Stephen M SM

IEEE transactions on medical imaging 20170921 1

We present a novel approach for improving the shape statistics of medical image objects by generating correspondence of skeletal points. Each object's interior is modeled by an s-rep, i.e., by a sampled, folded, two-sided skeletal sheet with spoke vectors proceeding from the skeletal sheet to the boundary. The skeleton is divided into three parts: the up side, the down side, and the fold curve. The spokes on each part are treated separately and, using spoke interpolation, are shifted along that ...[more]

PMID: 28945591

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Project description:IntroductionThe regular collection of 3-dimensional (3D) imaging data is critical to the development and implementation of accurate predictive models of facial skeletal growth. However, repeated exposure to x-ray-based modalities such as cone-beam computed tomography has unknown risks that outweigh many potential benefits, especially in pediatric patients. One solution is to make inferences about the facial skeleton from external 3D surface morphology captured using safe nonionizing imaging modalities alone. However, the degree to which external 3D facial shape is an accurate proxy of skeletal morphology has not been previously quantified. As a first step in validating this approach, we tested the hypothesis that population-level variation in the 3D shape of the face and skeleton significantly covaries.MethodsWe retrospectively analyzed 3D surface and skeletal morphology from a previously collected cross-sectional cone-beam computed tomography database of nonsurgical orthodontics patients and used geometric morphometrics and multivariate statistics to test the hypothesis that shape variation in external face and internal skeleton covaries.ResultsExternal facial morphology is highly predictive of variation in internal skeletal shape ([Rv] = 0.56, P <0.0001; partial least squares [PLS] 1-13 = 98.7% covariance, P <0.001) and asymmetry (Rv = 0.34, P <0.0001; PLS 1-5 = 90.2% covariance, P <0.001), whereas age-related (r(2) = 0.84, P <0.001) and size-related (r(2) = 0.67, P <0.001) shape variation was also highly correlated.ConclusionsSurface morphology is a reliable source of proxy data for the characterization of skeletal shape variation and thus is particularly valuable in research designs where reducing potential long-term risks associated with radiologic imaging methods is warranted. We propose that longitudinal surface morphology from early childhood through late adolescence can be a valuable source of data that will facilitate the development of personalized craniodental and treatment plans and reduce exposure levels to as low as reasonably achievable.

Dataset Information

Skeletal Shape Correspondence Through Entropy.

Publications

Skeletal Shape Correspondence Through Entropy.

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