Joshua Cates

Interactive, GPU-Based Level Sets for 3D Segmentation

While level sets have demonstrated a great potential for 3D medical image segmentation, their usefulness has been limited by two problems. First, 3D level sets are relatively slow to compute. Second, their formulation usually entails several free parameters which can be very difficult to correctly tune for specific applications. This paper presents a tool for 3D segmentation that relies on level-set surface models computed at interactive rates on commodity graphics cards (GPUs). The interactive rates for solving the level-set PDE give the user immediate feedback on the parameter settings, and thus users can tune three separate parameters and control the shape of the model in real time. We have found that this interactivity enables users to produce good, reliable segmentation, as supported by qualitative and quantitative results.

Shape modeling and analysis with entropy-based particle systems

This paper presents a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. The proposed method is to construct a point-based sampling of the shape ensemble that simultaneously maximizes both the geometric accuracy and the statistical simplicity of the model. Surface point samples, which also define the shape-to-shape correspondences, are modeled as sets of dynamic particles that are constrained to lie on a set of implicit surfaces. Sample positions are optimized by gradient descent on an energy function that balances the negative entropy of the distribution on each shape with the positive entropy of the ensemble of shapes. We also extend the method with a curvature-adaptive sampling strategy in order to better approximate the geometry of the objects. This paper presents the formulation; several synthetic examples in two and three dimensions; and an application to the statistical shape analysis of the caudate and hippocampus brain structures from two clinical studies.

GIST: an interactive, GPU-based level set segmentation tool for 3D medical images

While level sets have demonstrated a great potential for 3D medical image segmentation, their usefulness has been limited by two problems. First, 3D level sets are relatively slow to compute. Second, their formulation usually entails several free parameters which can be very difficult to correctly tune for specific applications. The second problem is compounded by the first. This paper describes a new tool for 3D segmentation that addresses these problems by computing level-set surface models at interactive rates. This tool employs two important, novel technologies. First is the mapping of a 3D level-set solver onto a commodity graphics card (GPU). This mapping relies on a novel mechanism for GPU memory management. The interactive rates level-set PDE solver give the user immediate feedback on the parameter settings, and thus users can tune free parameters and control the shape of the model in real time. The second technology is the use of intensity-based speed functions, which allow a user to quickly and intuitively specify the behavior of the deformable model. We have found that the combination of these interactive tools enables users to produce good, reliable segmentations. To support this observation, this paper presents qualitative results from several different datasets as well as a quantitative evaluation from a study of brain tumor segmentations.

Case study: an evaluation of user-assisted hierarchical watershed segmentation

This paper evaluates the effectiveness of an interactive, three-dimensional image segmentation technique that relies on watersheds. This paper presents two user-based case studies, which include two different groups of domain experts. Subjects manipulate a graphics-based front end to a hierarchy of segmented regions generated from a watershed segmentation algorithm, which is implemented in the Insight Toolkit. In the first study, medical students segment several different anatomical structures from the Visible Human Female head and neck color cryosection data. In the second study, radiologists use the interactive tool to produce models of brain tumors from MRI data. This paper presents a quantitative and qualitative comparison against hand contouring. To quantify accuracy, we estimate ground truth from the hand-contouring data using the Simultaneous Truth and Performance Estimation algorithm. We also apply metrics from the literature to estimate precision and efficiency. The watershed segmentation technique showed improved subject interaction times and increased inter-subject precision over hand contouring, with quality that is visually and statistically comparable. The analysis also identifies some failures in the watershed technique, where edges were poorly defined in the data, and note a trend in the hand-contouring results toward systematically larger segmentations, which raises questions about the wisdom of using expert segmentations to define ground truth.

Particle-Based Shape Analysis of Multi-object Complexes

This paper presents a new method for optimizing surface point correspondences for shape modeling of multiobject anatomy, or shape complexes. The proposed method is novel in that it optimizes correspondence positions in the full, joint shape space of the object complex. Researchers have previously only considered the correspondence problem separately for each structure, thus ignoring the interstructural shape correlations that are increasingly of interest in many clinical contexts, such as the study of the effects of disease on groups of neuroanatomical structures. The proposed method uses a nonparametric, dynamic particle system to simultaneously sample object surfaces and optimize correspondence point positions. This paper also suggests a principled approach to hypothesis testing using the Hotelling T2 test in the PCA space of the correspondence model, with a simulation-based choice of the number of PCA modes. We also consider statistical analysis of object poses. The modeling and analysis methods are illustrated on brain structure complexes from an ongoing clinical study of pediatric autism.

Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. In this paper we propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The statistical significance of the dependence is evaluated using permutation tests designed to estimate the likelihood of achieving the observed statistics under numerous rearrangements of the shape parameters with respect to the explanatory variable. We demonstrate the method on synthetic data and provide a new results on clinical MRI data related to early development of the human head.

Combined SPHARM-PDM and entropy-based particle systems shape analysis framework.

Description of purpose: The NA-MIC SPHARM-PDM Toolbox represents an automated set of tools for the computation of 3D structural statistical shape analysis. SPHARM-PDM solves the correspondence problem by defining a first order ellipsoid aligned, uniform spherical parameterization for each object with correspondence established at equivalently parameterized points. However, SPHARM correspondence has shown to be inadequate for some biological shapes that are not well described by a uniform spherical parameterization. Entropy-based particle systems compute correspondence by representing surfaces as discrete point sets that does not rely on any inherent parameterization. However, they are sensitive to initialization and have little ability to recover from initial errors. By combining both methodologies we compute reliable correspondences in topologically challenging biological shapes. Data: Diverse subcortical structures cohorts were used, obtained from MR brain images. Method(s): The SPHARM-PDM shape analysis toolbox was used to compute point based correspondent models that were then used as initializing particles for the entropy-based particle systems. The combined framework was implemented as a stand-alone Slicer3 module, which works as an end-to-end shape analysis module. Results: The combined SPHARM-PDM-Particle framework has demonstrated to improve correspondence in the example dataset over the conventional SPHARM-PDM toolbox. Conclusions: The work presented in this paper demonstrates a two-sided improvement for the scientific community, being able to 1) find good correspondences among spherically topological shapes, that can be used in many morphometry studies 2) offer an end-to-end solution that will facilitate the access to shape analysis framework to users without computer expertise.

Geodesic Distances to Landmarks for Dense Correspondence on Ensembles of Complex Shapes

Establishing correspondence points across a set of biomedical shapes is an important technology for a variety of applications that rely on statistical analysis of individual subjects and populations. The inherent complexity (e.g. cortical surface shapes) and variability (e.g. cardiac chambers) evident in many biomedical shapes introduce significant challenges in finding a useful set of dense correspondences. Application specific strategies, such as registration of simplified (e.g. inflated or smoothed) surfaces or relying on manually placed landmarks, provide some improvement but suffer from limitations including increased computational complexity and ambiguity in landmark placement. This paper proposes a method for dense point correspondence on shape ensembles using geodesic distances to a priori landmarks as features. A novel set of numerical techniques for fast computation of geodesic distances to point sets is used to extract these features. The proposed method minimizes the ensemble entropy based on these features, resulting in isometry invariant correspondences in a very general, flexible framework.

Automatic segmentation of the left atrium from MRI images using salient feature and contour evolution

We propose an automatic approach for segmenting the left atrium from MRI images. In particular, the thoracic aorta is detected and used as a salient feature to find a seed region that lies inside the left atrium. A hybrid energy that combines robust statistics and localized region intensity information is employed to evolve active contours from the seed region to capture the whole left atrium. The experimental results demonstrate the accuracy and robustness of our approach.

Group-wise automatic mesh-based analysis of cortical thickness

The analysis of neuroimaging data from pediatric populations presents several challenges. There are normal variations in brain shape from infancy to adulthood and normal developmental changes related to tissue maturation. Measurement of cortical thickness is one important way to analyze such developmental tissue changes. We developed a novel framework that allows group-wise automatic mesh-based analysis of cortical thickness. Our approach is divided into four main parts. First an individual pre-processing pipeline is applied on each subject to create genus-zero inflated white matter cortical surfaces with cortical thickness measurements. The second part performs an entropy-based group-wise shape correspondence on these meshes using a particle system, which establishes a trade-off between an even sampling of the cortical surfaces and the similarity of corresponding points across the population using sulcal depth information and spatial proximity. A novel automatic initial particle sampling is performed using a matched 98-lobe parcellation map prior to a particle-splitting phase. Third, corresponding re-sampled surfaces are computed with interpolated cortical thickness measurements, which are finally analyzed via a statistical vertex-wise analysis module. This framework consists of a pipeline of automated 3D Slicer compatible modules. It has been tested on a small pediatric dataset and incorporated in an open-source C++ based high-level module called GAMBIT. GAMBITs setup allows efficient batch processing, grid computing and quality control. The current research focuses on the use of an average template for correspondence and surface re-sampling, as well as thorough validation of the framework and its application to clinical pediatric studies.