Christopher L. Wyatt

Automatic segmentation of the colon for virtual colonoscopy

Virtual colonoscopy is a minimally invasive technique that enables early detection of colorectal polyps and cancer. Normally, a patients bowel is prepared with colonic lavage and gas insufflation prior to computed tomography scanning. An important step for 3D analysis of the image volume is segmentation of the colon. The high-contrast gas/tissue interface that exists in the colon lumen makes segmentation of the majority of the colon relatively easy; however, two factors inhibit automatic segmentation of the entire colon. First, the colon is not the only gas-filled organ in the data volume: lungs, small bowel, and stomach also meet this criterion. User-defined seed points placed in the colon lumen have previously been required to spatially isolate the colon. Second, portions of the colon lumen may be obstructed by peristalsis, large masses, and/or residual feces. These complicating factors require increased user interaction during the segmentation process to isolate additional colonic segments. To automate the segmentation of the colon, we have developed a method to locate seed points and segment the gas-filled lumen sections without user supervision. We have also developed an automated approach to improve lumen segmentation by digitally removing residual contrast-enhanced fluid. Experimental results with 20 patient volumes show that our method is accurate and reliable.

Deformable registration of supine and prone colons for computed tomographic colonography.

Computed tomographic colonography is a minimally invasive technique for detecting colorectal polyps and colon cancer. Most computed tomographic colonography protocols acquire both prone and supine images to improve the visualization of the lumen wall, reduce false-positives, and improve sensitivity. Comparisons between the prone and supine images can be improved by registration between the scans. In this paper, we propose registering colon lumens, segmented from prone and supine images, using feature matching of the colon centerline and nonrigid registration of the lumen shapes represented as distance functions. Experimental registration results (n = 21 subjects) show a correspondence accuracy of 13.77 ± 6.20 mm for a range of polyp sizes. The overlap in the registered lumen segmentations show an average Jaccard similarity coefficient of 0.915 ± 0.07.

X-ray phase-contrast imaging with three 2D gratings

X-ray imaging is of paramount importance for clinical and preclinical imaging but it is fundamentally restricted by the attenuation-based contrast mechanism, which has remained essentially the same since Roentgens discovery a century ago. Recently, based on the Talbot effect, groundbreaking work was reported using 1D gratings for X-ray phase-contrast imaging with a hospital-grade X-ray tube instead of a synchrotron or microfocused source. In this paper, we report an extension using 2D gratings that reduces the imaging time and increases the accuracy and robustness of phase retrieval compared to current grating-based phase-contrast techniques. Feasibility is demonstrated via numerical simulation.

Shoreline Mapping using an Omni-directional Camera for Autonomous Surface Vehicle Applications

Autonomous surface vehicles (ASVs) have the potential to operate for extended periods of time in coastal, estuarine, and riverine environments for a variety of scientific, environmental, and military applications. However, these environments are often highly dynamic and unstructured, and present new challenges for autonomous operations. Toward the goal of achieving truly autonomous long-term operations in highly unstructured maritime environments, we present a new approach to create a shoreline map with an ASV in real-time by combining the analysis of images from a single omni-directional camera and knowledge of the vehicles location and motion information

Neuroimaging of rodent and primate models of alcoholism: Initial reports from the Integrative Neuroscience Initiative on Alcoholism

Neuroimaging of animal models of alcoholism offers a unique path for translational research to the human condition. Animal models permit manipulation of variables that are uncontrollable in clinical, human investigation. This symposium, which took place at the annual meeting of the Research Society on Alcoholism in Vancouver, British Columbia, Canada, on June 29th, 2004, presented initial findings based on neuroimaging studies from the two centers of the Integrative Neuroscience Initiative on Alcoholism funded by the National Institute on Alcohol Abuse and Alcoholism. Effects of alcohol exposure were assessed with in vitro glucose metabolic imaging of rat brain, in vitro receptor imaging of monkey brain, in vivo magnetic resonance imaging of monkey brain, and in vivo magnetic resonance spectroscopic quantification of alcohol metabolism kinetics in rat brain.

Atlas-guided segmentation of vervet monkey brain MRI

The vervet monkey is an important nonhuman primate model that allows the study of isolated environmental factors in a controlled environment. Analysis of monkey MRI often suffers from lower quality images compared with human MRI because clinical equipment is typically used to image the smaller monkey brain and higher spatial resolution is required. This, together with the anatomical differences of the monkey brains, complicates the use of neuroimage analysis pipelines tuned for human MRI analysis. In this paper we developed an open source image analysis framework based on the tools available within the 3D Slicer software to support a biological study that investigates the effect of chronic ethanol exposure on brain morphometry in a longitudinally followed population of male vervets. We first developed a computerized atlas of vervet monkey brain MRI, which was used to encode the typical appearance of the individual brain structures in MRI and their spatial distribution. The atlas was then used as a spatial prior during automatic segmentation to process two longitudinal scans per subject. Our evaluation confirms the consistency and reliability of the automatic segmentation. The comparison of atlas construction strategies reveals that the use of a population-specific atlas leads to improved accuracy of the segmentation for subcortical brain structures. The contribution of this work is twofold. First, we describe an image processing workflow specifically tuned towards the analysis of vervet MRI that consists solely of the open source software tools. Second, we develop a digital atlas of vervet monkey brain MRIs to enable similar studies that rely on the vervet model.

Registration of Images With Varying Topology Using Embedded Maps

This paper presents registration via embedded maps (REM), a deformable registration algorithm for images with varying topology. The algorithm represents 3-D images as 4-D manifolds in a Riemannian space (referred to as embedded maps). Registration is performed as a surface evolution matching one embedded map to another using a diffusion process. The approach differs from those existing in that it takes an a priori estimation of image regions where topological changes are present, for example lesions, and generates a dense vector field representing both the shape and intensity changes necessary to match the images. The algorithm outputs both a diffeomorphic deformation field and an intensity displacement which corrects the intensity difference caused by topological changes. Multiple sets of experiments are conducted on magnetic resonance imaging (MRI) with lesions from OASIS and ADNI datasets. These images are registered to either a brain template or images of healthy individuals. An exemplar case registering a template to an MRI with tumor is also given. The resulting deformation fields were compared with those obtained using diffeomorphic demons, where topological changes are not modeled. These sets of experiments demonstrate the efficacy of our proposed REM method for registration of brain MRI with severe topological differences.

An automatic unsupervised classification of MR images in Alzheimer's disease

Image-analysis methods play an important role in helping detect brain changes in and diagnosis of Alzheimers Disease (AD). In this paper, we propose an automatic unsupervised classification approach to distinguish brain magnetic resonance (MR) images of AD patients from those of elderly normal controls. The symmetric log-domain diffeomorphic demons algorithm, with the properties of symmetry and invertibility, is used to compute the pair-wise registration, whose deformation field is then used to calculate the Riemannian distance between them. The spectral embedding algorithm is performed based on the Riemannian distance matrix to project images onto a low-dimensional space where each image is represented as a point and its neighboring points correspond to images of high anatomical similarity. Finally, the quick shift clustering method is employed in the embedded space to partition the dataset into subgroups. The experiments using the proposed method show very good performance for clustering images into AD and normal aging, using the Clinical Dementia Rating (CDR) scale as a comparison.

Registration of prone and supine colons in the presence of topological changes

CT colonography is a minimally-invasive screening technique for colorectal polyps in which X-ray CT images of the distended colon are acquired, usually in the prone and supine positions. Registration of segmented colons from both images will be useful for computer-assisted polyp detection. We have previously presented algorithms for registration of the prone and supine colon when both are well distended and there is a single connected lumen. However due to inadequate bowel preparation or peristalsis there may be collapsed segments in one or both of the colons resulting in a topological change in the images. Such changes make deformable registrations of the colons difficult, and at present there are no registration algorithms which can accommodate them. In this paper we present an algorithm which can perform volume registration of prone/supine colon images in the presence of a topological change.

Registration of images with topological change via riemannian embedding

In this paper, we develop a new deformable registration algorithm for images with pathology-induced topological changes. In this algorithm, 3D images are embedded as 4D surfaces in a Riemannian space and the registration is conducted as a surface evolution process. Our algorithm differs from existing methods in the sense that it takes an a-priori estimation of areas with topological change as an additional input and generates dense deformation vector fields which are free of false deformation. In particular, the output of our algorithm is composed of a diffeomorphic deformation field and an intensity displacement which corrects the intensity difference caused by the topological changes. The experiments demonstrate that our proposed algorithm is capable of accurately registering images with considerable topological changes. More importantly, the resulting deformation field is not impacted by topological changes, i.e., there is no false deformation.