ToScA North America (6 – 8 June 2017, The University of Texas, Austin, TX) Program

ToScA North America will address key areas of science, including Multi-modal Imaging, Geosciences, Forensics, Increasing Contrast, Educational Outreach, Data, Materials Science and Medical and Biological Science.University of Texas High-Resolution X-ray CT Facility (UTCT); Jackson School of Geosciences, The University of Texas at Austin; Natural History Museum (London); Royal Microscopical Society (Oxford, UK)Geological Science

Shape-driven segmentation of the arterial wall in intravascular ultrasound images

Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3D reconstruction, and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior, we utilize an intensity prior through a non-parametric probability density based image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach

Ground truth determination for segmentation of tomographic volumes using interpolation

Dissertação para obtenção do Grau de Mestre em Engenharia BiomédicaOptical projection tomographic microscopy allows for a 3D analysis of individual cells, making it possible to study its morphology. The 3D imagining technique used in this thesis uses white light excitation to image stained cells, and is referred to as single-cell optical computed tomography (cell CT). Studies have shown that morphological characteristics of the cell and its nucleus are deterministic in cancer diagnoses. For a more complete and accurate analysis of these characteristics, a fully-automated analysis of the single-cell 3D tomographic images can be done. The first step is segmenting the image into the different cell components. To assess how accurate the segmentation is, there is a need to determine ground truth of the automated segmentation. This dissertation intends to expose a method of obtaining ground truth for 3D segmentation of single cells. This was achieved by developing a software in CSharp. The software allows the user to input a visual segmentation of each 2D slice of a 3D volume by using a pen to trace the visually identified boundary of a cell component on a tablet. With this information, the software creates a segmentation of a 3D tomographic image that is a result of human visual segmentation. To increase the speed of this process, interpolation algorithms can be used. Since it is very time consuming to draw on every slice the user can skip slices. Interpolation algorithms are used to interpolate on the skipped slices. Five different interpolation algorithms were written: Linear Interpolation, Gaussian splat, Marching Cubes, Unorganized Points, and Delaunay Triangulation. To evaluate the performance of each interpolation algorithm the following evaluation metrics were used: Jaccard Similarity, Dice Coefficient, Specificity and Sensitivity.After evaluating each interpolation method we concluded that linear interpolation was the most accurate interpolation method, producing the best segmented volume for a faster ground truth determination method

Shape-driven segmentation of the arterial wall in intravascular ultrasound images

Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3D reconstruction, and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior, we utilize an intensity prior through a non-parametric probability density based image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach

Automatic centerline extraction of coronary arteries in coronary computed tomographic angiography

Coronary computed tomographic angiography (CCTA) is a non-invasive imaging modality for the visualization of the heart and coronary arteries. To fully exploit the potential of the CCTA datasets and apply it in clinical practice, an automated coronary artery extraction approach is needed. The purpose of this paper is to present and validate a fully automatic centerline extraction algorithm for coronary arteries in CCTA images. The algorithm is based on an improved version of Frangi’s vesselness filter which removes unwanted step-edge responses at the boundaries of the cardiac chambers. Building upon this new vesselness filter, the coronary artery extraction pipeline extracts the centerlines of main branches as well as side-branches automatically. This algorithm was first evaluated with a standardized evaluation framework named Rotterdam Coronary Artery Algorithm Evaluation Framework used in the MICCAI Coronary Artery Tracking challenge 2008 (CAT08). It includes 128 reference centerlines which were manually delineated. The average overlap and accuracy measures of our method were 93.7% and 0.30 mm, respectively, which ranked at the 1st and 3rd place compared to five other automatic methods presented in the CAT08. Secondly, in 50 clinical datasets, a total of 100 reference centerlines were generated from lumen contours in the transversal planes which were manually corrected by an expert from the cardiology department. In this evaluation, the average overlap and accuracy were 96.1% and 0.33 mm, respectively. The entire processing time for one dataset is less than 2 min on a standard desktop computer. In conclusion, our newly developed automatic approach can extract coronary arteries in CCTA images with excellent performances in extraction ability and accuracy

Display and Analysis of Tomographic Reconstructions of Multiple Synthetic Aperture LADAR (SAL) images

Synthetic aperture ladar (SAL) is similar to synthetic aperture radar (SAR) in that it can create range/cross-range slant plane images of the illuminated scatters; however, SAL has wavelengths 10,000x smaller than SAR enabling a relatively narrow real aperture, diffraction limited beam widths. The relatively narrow real aperture resolutions allow for multiple slant planes to be created for a single target with reasonable range/aperture combinations. These multiple slant planes can be projected into a single slant plane projections (as in SAR). It can also be displayed as a 3-D image with asymmetric resolutions, diffraction limited in the dimension orthogonal to the SAL baseline. Multiple images with diversity in angle orthogonal to SAL baselines can be used to synthesize resolution with tomographic techniques and enhance the diffraction limited resolution. The goal of this research is to explore methods to enhance the diffraction limited resolutions with multiple observations and/or multiple slant plane imaging with SAL systems. Specifically, metrics associated with the information content of the tomographic based 3 dimensional reconstructions of SAL intensity imagery will be investigated to see how it changes as a function of number of slant planes in the SAL images and number of elevation observations are varied

Pore network analysis of Brae Formation sandstone, North Sea

In this work, we apply digital rock physics (DRP) to characterize the pore networks of the Brae Formation sandstones from two different wells in the Miller field area (North Sea, UK). Using X-ray micro-CT scans, we calculate the porosity and permeability and generate pore network models to assess pore shape characteristics. The porous samples are marked by macroporosities ranging from 4.9% to 15.2% with the effective porosities varying from 0 to 14.8%. The samples also contained some microporosity hosted in secondary and accessory mineral phases, varying between 2.6% and 10.7%. Pore network model results for total porosity indicate that the samples have median pore and throat radii ranging from 5.5 μm to 16.8 μm and 6.4 μm–12.9 μm, respectively. The throat length of all samples has a median value ranging between 36.3 μm and 82.4 μm. The ratio between effective porosity and total porosity (φ∗) varies with total porosity (φ) following the exponential relation φ∗ = 0.98 − e− (φ− 0.032)/0.028. Pore network connectivity is established at a porosity of 3% and full communication is achieved at porosities exceeding 10%. Permeability was found to vary with total porosity with an exponent of 3.67. Based on these observations and the results from our models, the connectivity of the pore network has important implications for predicting reservoir performance during large scale subsurface projects such as hydrocarbon production and CO2 storage