Application of Uncertainty Modeling Frameworks to Uncertain Isosurface Extraction

Abstract. Proper characterization of uncertainty is a challenging task. Depend-ing on the sources of uncertainty, various uncertainty modeling frameworks have been proposed and studied in the uncertainty quantification literature. This pa-per applies various uncertainty modeling frameworks, namely possibility theory, Dempster-Shafer theory and probability theory to isosurface extraction from un-certain scalar fields. It proposes an uncertainty-based marching cubes template as an abstraction of the conventional marching cubes algorithm with a flexible uncertainty measure. The applicability of the template is demonstrated using 2D simulation data in weather forecasting and computational fluid dynamics and a synthetic 3D dataset

09251 Abstracts Collection -- Scientific Visualization

From 06-14-2009 to 06-19-2009, the Dagstuhl Seminar 09251 ``Scientific Visualization \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, over 50 international participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general

Doctor of Philosophy

dissertationIn this dissertation, we advance the theory and practice of verifying visualization algorithms. We present techniques to assess visualization correctness through testing of important mathematical properties. Where applicable, these techniques allow us to distinguish whether anomalies in visualization features can be attributed to the underlying physical process or to artifacts from the implementation under verification. Such scientific scrutiny is at the heart of verifiable visualization - subjecting visualization algorithms to the same verification process that is used in other components of the scientific pipeline. The contributions of this dissertation are manifold. We derive the mathematical framework for the expected behavior of several visualization algorithms, and compare them to experimentally observed results in the selected codes. In the Computational Science & Engineering community CS&E, this technique is know as the Method of Manufactured Solution (MMS). We apply MMS to the verification of geometrical and topological properties of isosurface extraction algorithms, and direct volume rendering. We derive the convergence of geometrical properties of isosurface extraction techniques, such as function value and normals. For the verification of topological properties, we use stratified Morse theory and digital topology to design algorithms that verify topological invariants. In the case of volume rendering algorithms, we provide the expected discretization errors for three different error sources. The results of applying the MMS is another important contribution of this dissertation. We report unexpected behavior for almost all implementations tested. In some cases, we were able to find and fix bugs that prevented the correctness of the visualization algorithm. In particular, we address an almost 2 0 -year-old bug with the core disambiguation procedure of Marching Cubes 33, one of the first algorithms intended to preserve the topology of the trilinear interpolant. Finally, an important by-product of this work is a range of responses practitioners can expect to encounter with the visualization technique under verification

From Fully-Supervised Single-Task to Semi-Supervised Multi-Task Deep Learning Architectures for Segmentation in Medical Imaging Applications

Medical imaging is routinely performed in clinics worldwide for the diagnosis and treatment of numerous medical conditions in children and adults. With the advent of these medical imaging modalities, radiologists can visualize both the structure of the body as well as the tissues within the body. However, analyzing these high-dimensional (2D/3D/4D) images demands a significant amount of time and effort from radiologists. Hence, there is an ever-growing need for medical image computing tools to extract relevant information from the image data to help radiologists perform efficiently. Image analysis based on machine learning has pivotal potential to improve the entire medical imaging pipeline, providing support for clinical decision-making and computer-aided diagnosis. To be effective in addressing challenging image analysis tasks such as classification, detection, registration, and segmentation, specifically for medical imaging applications, deep learning approaches have shown significant improvement in performance. While deep learning has shown its potential in a variety of medical image analysis problems including segmentation, motion estimation, etc., generalizability is still an unsolved problem and many of these successes are achieved at the cost of a large pool of datasets. For most practical applications, getting access to a copious dataset can be very difficult, often impossible. Annotation is tedious and time-consuming. This cost is further amplified when annotation must be done by a clinical expert in medical imaging applications. Additionally, the applications of deep learning in the real-world clinical setting are still limited due to the lack of reliability caused by the limited prediction capabilities of some deep learning models. Moreover, while using a CNN in an automated image analysis pipeline, it’s critical to understand which segmentation results are problematic and require further manual examination. To this extent, the estimation of uncertainty calibration in a semi-supervised setting for medical image segmentation is still rarely reported. This thesis focuses on developing and evaluating optimized machine learning models for a variety of medical imaging applications, ranging from fully-supervised, single-task learning to semi-supervised, multi-task learning that makes efficient use of annotated training data. The contributions of this dissertation are as follows: (1) developing a fully-supervised, single-task transfer learning for the surgical instrument segmentation from laparoscopic images; and (2) utilizing supervised, single-task, transfer learning for segmenting and digitally removing the surgical instruments from endoscopic/laparoscopic videos to allow the visualization of the anatomy being obscured by the tool. The tool removal algorithms use a tool segmentation mask and either instrument-free reference frames or previous instrument-containing frames to fill in (inpaint) the instrument segmentation mask; (3) developing fully-supervised, single-task learning via efficient weight pruning and learned group convolution for accurate left ventricle (LV), right ventricle (RV) blood pool and myocardium localization and segmentation from 4D cine cardiac MR images; (4) demonstrating the use of our fully-supervised memory-efficient model to generate dynamic patient-specific right ventricle (RV) models from cine cardiac MRI dataset via an unsupervised learning-based deformable registration field; and (5) integrating a Monte Carlo dropout into our fully-supervised memory-efficient model with inherent uncertainty estimation, with the overall goal to estimate the uncertainty associated with the obtained segmentation and error, as a means to flag regions that feature less than optimal segmentation results; (6) developing semi-supervised, single-task learning via self-training (through meta pseudo-labeling) in concert with a Teacher network that instructs the Student network by generating pseudo-labels given unlabeled input data; (7) proposing largely-unsupervised, multi-task learning to demonstrate the power of a simple combination of a disentanglement block, variational autoencoder (VAE), generative adversarial network (GAN), and a conditioning layer-based reconstructor for performing two of the foremost critical tasks in medical imaging — segmentation of cardiac structures and reconstruction of the cine cardiac MR images; (8) demonstrating the use of 3D semi-supervised, multi-task learning for jointly learning multiple tasks in a single backbone module – uncertainty estimation, geometric shape generation, and cardiac anatomical structure segmentation of the left atrial cavity from 3D Gadolinium-enhanced magnetic resonance (GE-MR) images. This dissertation summarizes the impact of the contributions of our work in terms of demonstrating the adaptation and use of deep learning architectures featuring different levels of supervision to build a variety of image segmentation tools and techniques that can be used across a wide spectrum of medical image computing applications centered on facilitating and promoting the wide-spread computer-integrated diagnosis and therapy data science

Ovis: A framework for visual analysis of ocean forecast ensembles

pre-printWe present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations of the sea surface height that is used in ocean forecasting. The position of eddies can be derived directly from the sea surface height and our visualization approach enables their interactive exploration and analysis.The behavior of eddies is important in different application settings of which we present two in this paper. First, we show an application for interactive planning of placement as well as operation of off-shore structures using real-world ensemble simulation data of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by eddies, and the oil and gas industry relies on ocean forecasts for efficient operations. We enable analysis of the spatial domain, as well as the temporal evolution, for planning the placement and operation of structures.Eddies are also important for marine life. They transport water over large distances and with it also heat and other physical properties as well as biological organisms. In the second application we present the usefulness of our tool, which could be used for planning the paths of autonomous underwater vehicles, so called gliders, for marine scientists to study simulation data of the largely unexplored Red Sea

A geographic information system (GIS) based modeling support system for air quality analysis.

by Shu Keung Choi.Thesis (M.Phil.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 145-150).ABSTRACT --- p.iACKNOWLEDGMENTS --- p.iiiTABLE OF CONTENTS --- p.ivLIST OF FIGURES --- p.ixLIST OF PLATES --- p.xiLIST OF TABLES --- p.xiiChapter CHAPTER 1. --- INTRODUCTION --- p.1Chapter 1.1 --- Concerns on Current Air Quality Modeling Process --- p.2Chapter 1.2 --- Objective --- p.2Chapter 1.3 --- Rationale --- p.4Chapter 1.4 --- System Overview --- p.4Chapter 1.5 --- Questions --- p.6Chapter 1.6 --- Thesis Organization --- p.6Chapter CHAPTER 2. --- LITERATURE REVIEW --- p.8Chapter 2.1 --- Introduction --- p.8Chapter 2.2 --- Geographic Information System --- p.8Chapter 2.2.1 --- Data Assimilation --- p.9Chapter 2.2.1.1 --- Data Representation --- p.9Chapter 2.2.1.2 --- Data Format --- p.10Chapter 2.2.1.3 --- Data Alignment --- p.11Chapter 2.2.2 --- Modeling Support --- p.11Chapter 2.3 --- Environmental Modeling --- p.12Chapter 2.4 --- Integration of GIS and Environmental Modeling --- p.15Chapter 2.4.1 --- The Need for Integration --- p.15Chapter 2.4.2 --- Forms of Integration --- p.17Chapter 2.5 --- Air Quality Modeling --- p.20Chapter 2.5.1 --- Classes of Models --- p.21Chapter 2.5.1.1 --- Classification by Spatial Scale --- p.21Chapter 2.5.1.2 --- Classification by Modeling Approach --- p.22Chapter 2.6 --- Gaussian Plume Models --- p.24Chapter 2.6.1 --- Formulation --- p.24Chapter 2.6.2 --- Determination of σy and σz --- p.25Chapter 2.6.3 --- The Stability Classification --- p.26Chapter 2.6.4 --- Estimation of σy and σz --- p.27Chapter 2.6.5 --- Assumptions in the Gaussian Model --- p.30Chapter 2.7 --- Air Quality Model Evaluation --- p.31Chapter 2.7.1 --- Model Uncertainties --- p.31Chapter 2.7.1.1 --- Inherent Uncertainty --- p.31Chapter 2.7.1.2 --- Reducible Uncertainty Errors --- p.33Chapter 2.7.1.2.1 --- Meteorological Data Errors --- p.33Chapter 2.7.1.2.2 --- Emission Data Errors --- p.34Chapter 2.7.1.2.3 --- Model Errors --- p.34Chapter 2.7.2 --- Operational Performance Evaluation --- p.36Chapter 2.7.2.1 --- Woods Hole Performance Measures --- p.36Chapter 2.7.2.2 --- Fractional Bias and Fractional Scatter --- p.38Chapter 2.7.2.3 --- Measuring the Normalized Ratios --- p.39Chapter 2.7.2.4 --- Combination of Statistical Measures --- p.40Chapter 2.8 --- Visualization --- p.43Chapter 2.8.1 --- Visualization Software Framework --- p.43Chapter 2.8.2 --- GIS and Visualization --- p.46Chapter 2.9 --- Conclusion --- p.47Chapter CHAPTER 3. --- SYSTEM DESIGN --- p.48Chapter 3.1 --- System Overview --- p.48Chapter 3.2 --- Supported Models --- p.50Chapter 3.3 --- System Software Platforms --- p.51Chapter 3.3.1 --- ARC/INFO --- p.52Chapter 3.3.1.1 --- Overview --- p.52Chapter 3.3.1.2 --- The Role in the System --- p.53Chapter 3.3.2 --- Advanced Visualization System (AVS) --- p.54Chapter 3.3.2.1 --- Overview --- p.54Chapter 3.3.2.2 --- The Role in the System --- p.54Chapter 3.4 --- System Requirements and Specification --- p.56Chapter 3.4.1 --- Notation --- p.56Chapter 3.4.2 --- Data Preprocessing --- p.57Chapter 3.4.3 --- Data Postprocessing --- p.63Chapter 3.4.4 --- Model Performance Evaluation --- p.68Chapter 3.4.5 --- Visualization --- p.74Chapter 3.4.5.1 --- Reading ARC/INFO Data --- p.74Chapter 3.4.5.2 --- Applying Visualization Techniques --- p.77Chapter 3.4.5.2.1 --- Surface Mesh --- p.77Chapter 3.4.5.2.2 --- Multi-window Approach --- p.79Chapter 3.5 --- Data File Format --- p.85Chapter CHAPTER 4. --- A TEST CASE --- p.92Chapter 4.1 --- Introduction --- p.92Chapter 4.2 --- Test Case Components --- p.92Chapter 4.2.1 --- Study Area --- p.92Chapter 4.2.2 --- Source Data --- p.93Chapter 4.2.3 --- Air Quality Model - MPTER --- p.93Chapter 4.2.4 --- Meteorological Data Preprocessor - RAMMET --- p.95Chapter 4.3 --- Executing the Test Case --- p.95Chapter 4.3.1 --- Main Menu --- p.95Chapter 4.3.2 --- Viewing the study area --- p.96Chapter 4.3.3 --- Data Preprocessing --- p.96Chapter 4.3.3.1 --- Define Data Mapper --- p.98Chapter 4.3.3.2 --- Execute Data Preprocessor --- p.101Chapter 4.3.3.3 --- Meteorological Data Preprocessing --- p.102Chapter 4.3.3.4 --- Model Input File Editing --- p.103Chapter 4.3.3.5 --- Discussions --- p.105Chapter 4.3.4 --- Model Execution --- p.107Chapter 4.3.5 --- Data Postprocessing --- p.107Chapter 4.3.5.1 --- Import Model Result to GIS --- p.108Chapter 4.3.5.2 --- Iso-line of Concentration Map --- p.108Chapter 4.3.5.3 --- Discussions --- p.109Chapter 4.3.6 --- Model Performance Evaluation --- p.112Chapter 4.3.6.1 --- Program Execution --- p.113Chapter 4.3.6.2 --- Discussions --- p.113Chapter 4.3.7 --- Visualization --- p.116Chapter 4.3.7.1 --- Surface Mesh --- p.116Chapter 4.3.7.2 --- Multi-window Approach for 4D Data set --- p.117Chapter 4.3.7.2.1 --- Overview --- p.117Chapter 4.3.7.2.2 --- Overall Controls and Relations between Viewers --- p.121Chapter 4.3.7.2.3 --- Independent Controls within Each Viewers --- p.122Chapter 4.3.7.2.4 --- "The x,y,z-volume Viewer" --- p.123Chapter 4.3.7.2.5 --- "x,y,t-volume in ViewerZ" --- p.128Chapter 4.3.7.2.6 --- Other Viewers --- p.132Chapter 4.3.7.3 --- Discussions --- p.134Chapter 4.4 --- Conclusion --- p.137Chapter CHAPTER 5. --- CONCLUSION --- p.138Chapter 5.1 --- System Design Summary --- p.138Chapter 5.2 --- Summary of the Functions --- p.139Chapter 5.2.1 --- Data Preprocessing --- p.139Chapter 5.2.2 --- Data Postprocessing --- p.140Chapter 5.2.3 --- Model Evaluation --- p.140Chapter 5.2.4 --- Visualization --- p.141Chapter 5.3 --- Further Research --- p.143BIBLIOGRAPHY --- p.14

Proceedings, MSVSCC 2013

Proceedings of the 7th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 11, 2013 at VMASC in Suffolk, Virginia