Variable Scale Statistics For Cardiac Segmentation and Shape Analysis

A novel framework for medical image analysis, known as Shells and Spheres, has been developed by our research lab. This framework utilizes spherical operators of variable radius, centered at each image pixel and sized to reach, but not cross, the nearest boundary. Statistical population tests are performed on the populations of pixels within adjacent spheres to compare image regions across boundaries, delineating bothindependent image objects and the boundaries between them. This research has focused on developing the Shells and Spheres frameworkand applying it to the problem of segmentation of anatomical objects. Furthermore, we have rigorously studied the framework and its applications to clinical segmentation, validating and improving our n-dimensional segmentation algorithm. To this end, we have enhanced the original Shells and Spheres segmentation algorithm by adding a priori information, developing techniques for optimizing algorithm parameters, implementing a software platform for experimentation, and performing validation experiments using real 3D ovine cardiac MRI data. The system developed provides automated 3D segmentation given a priori information in the form of a trivial 2D manual training procedure, which involves tracing a single 2D contour from which 3D algorithm parameters are then automatically derived. We apply this system tosegmentation of the Right Ventricular Outflow Tract (RVOT) to aid in research toward the creation of a Tissue Engineered Pulmonary Valve(TEPV). Experimental methods are presented for the development and validation of the system, as well as a detailed description of the Shells and Spheres framework, our segmentation algorithm, and the clinical significance of this work

Shells and Spheres: An n-Dimensional Framework for Medial-Based Image Segmentation

We have developed a method for extracting anatomical shape models from n-dimensional images using an image analysis framework we call Shells and Spheres. This framework utilizes a set of spherical operators centered at each image pixel, grown to reach, but not cross, the nearest object boundary by incorporating “shells” of pixel intensity values while analyzing intensity mean, variance, and first-order moment. Pairs of spheres on opposite sides of putative boundaries are then analyzed to determine boundary reflectance which is used to further constrain sphere size, establishing a consensus as to boundary location. The centers of a subset of spheres identified as medial (touching at least two boundaries) are connected to identify the interior of a particular anatomical structure. For the automated 3D algorithm, the only manual interaction consists of tracing a single contour on a 2D slice to optimize parameters, and identifying an initial point within the target structure

Development and evaluation of a novel method for in-situ medical image display

Three-dimensional (3D) medical imaging, including computed tomography (CT) and magnetic resonance (MR), and other modalities, has become a standard of care for diagnosis of disease and guidance of interventional procedures. As the technology to acquire larger, more magnificent, and more informative medical images advances, so too must the technology to display, interact with, and interpret these data.This dissertation concerns the development and evaluation of a novel method for interaction with 3D medical images called "grab-a-slice," which is a movable, tracked stereo display. It is the latest in a series of displays developed in our laboratory that we describe as in-situ, meaning that the displayed image is embedded in a physical 3D coordinate system. As the display is moved through space, a continuously updated tomographic slice of a 3D medical image is shown on the screen, corresponding to the position and orientation of the display. The act of manipulating the display through a "virtual patient" preserves the perception of 3D anatomic relationships in a way that is not possible with conventional, fixed displays. The further addition of stereo display capabilities permits augmentation of the tomographic image data with out-of-plane structures using 3D graphical methods.In this dissertation we describe the research and clinical motivations for such a device. We describe the technical development of grab-a-slice as well as psychophysical experiments to evaluate the hypothesized perceptual and cognitive benefits. We speculate on the advantages and limitations of the grab-a-slice display and propose future directions for its use in psychophysical research, clinical settings, and image analysis

Feature-Based Correspondences to Infer the Location of Anatomical Landmarks

A methodology has been developed for automatically determining inter-image correspondences between cliques of features extracted from a reference and a query image. Cliques consist of up to threefeatures and correspondences between them are determined via a hierarchy of similarity metrics based on the inherent properties of the features and geometric relationships between those features. As opposed to approaches that determine correspondences solely by voxel intensity, features that also include shape description are used. Specifically, medial-based features areemployed because they are sparse compared to the number of image voxels and can be automatically extracted from the image.The correspondence framework has been extended to automatically estimate the location of anatomical landmarks in the query image by adding landmarks to the cliques. Anatomical landmark locationsare then inferred from the reference image by maximizing landmark correspondences. The ability to infer landmark locations has provided a means to validate the correspondence framework in thepresence of structural variation between images. Moreover, automated landmark estimation imparts the user with anatomical information and can hypothetically be used to initialize andconstrain the search space of segmentation and registration methods.Methods developed in this dissertation were applied to simulated MRI brain images, synthetic images, and images constructed from several variations of a parametric model. Results indicate that the methods are invariant to global translation and rotation and can operate in the presence of structure variation between images.The automated landmark placement method was shown to be accurate as compared to ground-truth that was established both parametrically and manually. It is envisioned that these automated methods could prove useful for alleviating time-consuming and tedious tasks in applications that currently require manual input, and eliminate intra-user subjectivity

Ultrasound imaging system combined with multi-modality image analysis algorithms to monitor changes in anatomical structures

This dissertation concerns the development and validation of an ultrasound imaging system and novel image analysis algorithms applicable to multiple imaging modalities. The ultrasound imaging system will include a framework for 3D volume reconstruction of freehand ultrasound: a mechanism to register the 3D volumes across time and subjects, as well as with other imaging modalities, and a playback mechanism to view image slices concurrently from different acquisitions that correspond to the same anatomical region. The novel image analysis algorithms include a noise reduction method that clusters pixels into homogenous patches using a directed graph of edges between neighboring pixels, a segmentation method that creates a hierarchical graph structure using statistical analysis and a voting system to determine the similarity between homogeneous patches given their neighborhood, and finally, a hybrid atlas-based registration method that makes use of intensity corrections induced at anatomical landmarks to regulate deformable registration. The combination of the ultrasound imaging system and the image analysis algorithms will provide the ability to monitor nerve regeneration in patients undergoing regenerative, repair or transplant strategies in a sequential, non-invasive manner, including visualization of registered real-time and pre-acquired data, thus enabling preventive and therapeutic strategies for nerve regeneration in Composite Tissue Allotransplantation (CTA). The registration algorithm is also applied to MR images of the brain to obtain reliable and efficient segmentation of the hippocampus, which is a prominent structure in the study of diseases of the elderly such as vascular dementia, Alzheimer’s, and late life depression. Experimental results on 2D and 3D images, including simulated and real images, with illustrations visualizing the intermediate outcomes and the final results are presented.

Statistical and deep learning methods for geoscience problems

Machine learning is the new frontier for technology development in geosciences and has developed extremely fast in the past decade. With the increased compute power provided by distributed computing and Graphics Processing Units (GPUs) and their exploitation provided by machine learning (ML) frameworks such as Keras, Pytorch, and Tensorflow, ML algorithms can now solve complex scientific problems. Although powerful, ML algorithms need to be applied to suitable problems conditioned for optimal results. For this reason ML algorithms require not only a deep understanding of the problem but also of the algorithm’s ability. In this dissertation, I show that Simple statistical techniques can often outperform ML-based models if applied correctly. In this dissertation, I show the success of deep learning in addressing two difficult problems. In the first application I use deep learning to auto-detect the leaks in a carbon capture project using pressure field data acquired from the DOE Cranfield site in Mississippi. I use the history of pressure, rates, and cumulative injection volumes to detect leaks as pressure anomaly. I use a different deep learning workflow to forecast high-energy electrons in Earth’s outer radiation belt using in situ measurements of different space weather parameters such as solar wind density and pressure. I focus on predicting electron fluxes of 2 MeV and higher energy and introduce the ensemble of deep learning models to further improve the results as compared to using a single deep learning architecture. I also show an example where a carefully constructed statistical approach, guided by the human interpreter, outperforms deep learning algorithms implemented by others. Here, the goal is to correlate multiple well logs across a survey area in order to map not only the thickness, but also to characterize the behavior of stacked gamma ray parasequence sets. Using tools including maximum likelihood estimation (MLE) and dynamic time warping (DTW) provides a means of generating quantitative maps of upward fining and upward coarsening across the oil field. The ultimate goal is to link such extensive well control with the spectral attribute signature of 3D seismic data volumes to provide a detailed maps of not only the depositional history, but also insight into lateral and vertical variation of mineralogy important to the effective completion of shale resource plays

Predicting multibody assembly of proteins

textThis thesis addresses the multi-body assembly (MBA) problem in the context of protein assemblies. [...] In this thesis, we chose the protein assembly domain because accurate and reliable computational modeling, simulation and prediction of such assemblies would clearly accelerate discoveries in understanding of the complexities of metabolic pathways, identifying the molecular basis for normal health and diseases, and in the designing of new drugs and other therapeutics. [...] [We developed] F²Dock (Fast Fourier Docking) which includes a multi-term function which includes both a statistical thermodynamic approximation of molecular free energy as well as several of knowledge-based terms. Parameters of the scoring model were learned based on a large set of positive/negative examples, and when tested on 176 protein complexes of various types, showed excellent accuracy in ranking correct configurations higher (F² Dock ranks the correcti solution as the top ranked one in 22/176 cases, which is better than other unsupervised prediction software on the same benchmark). Most of the protein-protein interaction scoring terms can be expressed as integrals over the occupied volume, boundary, or a set of discrete points (atom locations), of distance dependent decaying kernels. We developed a dynamic adaptive grid (DAG) data structure which computes smooth surface and volumetric representations of a protein complex in O(m log m) time, where m is the number of atoms assuming that the smallest feature size h is [theta](r[subscript max]) where r[subscript max] is the radius of the largest atom; updates in O(log m) time; and uses O(m)memory. We also developed the dynamic packing grids (DPG) data structure which supports quasi-constant time updates (O(log w)) and spherical neighborhood queries (O(log log w)), where w is the word-size in the RAM. DPG and DAG together results in O(k) time approximation of scoring terms where k << m is the size of the contact region between proteins. [...] [W]e consider the symmetric spherical shell assembly case, where multiple copies of identical proteins tile the surface of a sphere. Though this is a restricted subclass of MBA, it is an important one since it would accelerate development of drugs and antibodies to prevent viruses from forming capsids, which have such spherical symmetry in nature. We proved that it is possible to characterize the space of possible symmetric spherical layouts using a small number of representative local arrangements (called tiles), and their global configurations (tiling). We further show that the tilings, and the mapping of proteins to tilings on arbitrary sized shells is parameterized by 3 discrete parameters and 6 continuous degrees of freedom; and the 3 discrete DOF can be restricted to a constant number of cases if the size of the shell is known (in terms of the number of protein n). We also consider the case where a coarse model of the whole complex of proteins are available. We show that even when such coarse models do not show atomic positions, they can be sufficient to identify a general location for each protein and its neighbors, and thereby restricts the configurational space. We developed an iterative refinement search protocol that leverages such multi-resolution structural data to predict accurate high resolution model of protein complexes, and successfully applied the protocol to model gp120, a protein on the spike of HIV and currently the most feasible target for anti-HIV drug design.Computer Science

Mathematical Imaging and Surface Processing

Within the last decade image and geometry processing have become increasingly rigorous with solid foundations in mathematics. Both areas are research fields at the intersection of different mathematical disciplines, ranging from geometry and calculus of variations to PDE analysis and numerical analysis. The workshop brought together scientists from all these areas and a fruitful interplay took place. There was a lively exchange of ideas between geometry and image processing applications areas, characterized in a number of ways in this workshop. For example, optimal transport, first applied in computer vision is now used to define a distance measure between 3d shapes, spectral analysis as a tool in image processing can be applied in surface classification and matching, and so on. We have also seen the use of Riemannian geometry as a powerful tool to improve the analysis of multivalued images. This volume collects the abstracts for all the presentations covering this wide spectrum of tools and application domains