Shape-Based Models for Interactive Segmentation of Medical Images

Accurate image segmentation is one of the key problems in computer vision. In domains such as radiation treatment planning, dosimetrists must manually trace the outlines of a few critical structures on large numbers of images. Considerable similarity can be seen in the shape of these regions, both between adjacent slices in a particular patient and across the spectrum of patients. Consequently we should be able to model this similarity and use it to assist in the process of segmentation. Previous work has demonstrated that a constraint-based 2D radial model can capture generic shape information for certain shape classes, and can reduce user interaction by a factor of three over purely manual segmentation. Additional simulation studies have shown that a probabilistic version of the model has the potential to further reduce user interaction. This paper describes an implementation of both models in a general-purpose imaging and graphics framework and compares the usefulness of the models on several shape classes

Distributed XQuery

XQuery is increasingly being used for ad-hoc integration of heterogeneous data sources that are logically mapped to XML. For example, scientists need to query multiple scientific databases, which are distributed over a large geographic area, and it is possible to use XQuery for that. However, the language currently supports only the data shipping query evaluation model (through the document() function): it fetches all data sources to a single server, then runs the query there. This is a major limitation for many applications, especially when some data sources are very large, or when a data source is only a virtual XML view over some other logical data model. We propose here a simple extension to XQuery that allows query shipping to be expressed in the language, in addition to data shipping

Shape-Based Interactive Three-Dimensional Medical Image Segmentation

Accurate image segmentation continues to be one of the biggest challenges in medical image analysis. Simple, low-level vision techniques have had limited success in this domain because of the visual complexity of medical images. This paper presents a 3-D shape model that uses prior knowledge of an object's structure to guide the search for its boundaries. The shape model has been incorporated into SCANNER, an interactive software package for image segmentation. We describe a graphical user interface that was developed for finding the surface of the brain and explain how the 3-D model assists with the segmentation process. Preliminary experiments show that with this shape-based approach, a low-resolution boundary for a surface can be found with two-thirds less work for the user than with a comparable manual method

Using 3-D Shape Models to Guide Segmentation of MR Brain Images

Accurate segmentation of medical images poses one of the major challenges in computer vision. Approaches that rely solely on intensity information frequently fail because similar intensity values appear in multiple structures. This paper presents a method for using shape knowledge to guide the segmentation process, applying it to the task of finding the surface of the brain. A 3-D model that includes local shape constraints is fitted to an MR volume dataset. The resulting low-resolution surface is used to mask out regions far from the cortical surface, enabling an isosurface extraction algorithm to isolate a more detailed surface boundary. The surfaces generated by this technique are comparable to those achieved by other methods, without requiring user adjustment of a large number of ad hoc parameters

Design of an Anatomy Information System

Biology and medicine rely fundamentally on anatomy. Not only do you need anatomical knowledge to understand normal and abnormal function, anatomy also provides a framework for organizing other kinds of biomedical data. That’s why medical and other health sciences students take anatomy as one of their first courses. The Digital Anatomist Project undertaken by members of the University of Washington Structural Informatics Group aims to “put anatomy on a computer” in such a way that anatomical information becomes as fundamental to biomedical information management as the study of anatomy is to medical students. To do this we need to develop methods for representing anatomical information, accessing it, and reusing it in multiple applications ranging from education to clinical practice. This development process engenders many of the core research areas in biological structural informatics, which we have defined as a subfield of medical informatics dealing with information about the physical organization of the body. By its nature, structural information proves highly amenable to representation and visualization by computer graphics methods. In fact, computer graphics offers the first real breakthrough in anatomical knowledge representation since publication of the first scholarly anatomical treatise in 1546, in that it provides a means for capturing the 3D dynamic nature of the human body. In this article we explain the nature of anatomical information and discuss the design of a system to organize and access it. Example applications show the potential for reusing the same information in contexts ranging from education to clinical medicine, as well as the role of graphics in visualizing and interacting with anatomical representations

Simulations of the Microwave Sky

We create realistic, full-sky, half-arcminute resolution simulations of the microwave sky matched to the most recent astrophysical observations. The primary purpose of these simulations is to test the data reduction pipeline for the Atacama Cosmology Telescope (ACT) experiment; however, we have widened the frequency coverage beyond the ACT bands to make these simulations applicable to other microwave background experiments. Some of the novel features of these simulations are that the radio and infrared galaxy populations are correlated with the galaxy cluster populations, the CMB is lensed by the dark matter structure in the simulation via a ray-tracing code, the contribution to the thermal and kinetic Sunyaev-Zel'dovich (SZ) signals from galaxy clusters, groups, and the IGM has been included, and the gas prescription to model the SZ signals matches the most recent X-ray observations. Regarding the contamination of cluster SZ flux by radio galaxies, we find for 148 GHz (90 GHz) only 3% (4%) of halos have their SZ decrements contaminated at a level of 20% or more. We find the contamination levels higher for infrared galaxies. However, at 90 GHz, less than 20% of clusters with M_{200} > 2.5 x 10^{14} Msun and z<1.2 have their SZ decrements filled in at a level of 20% or more. At 148 GHz, less than 20% of clusters with M_{200} > 2.5 x 10^{14} Msun and z<0.8 have their SZ decrements filled in at a level of 50% or larger. Our models also suggest that a population of very high flux infrared galaxies, which are likely lensed sources, contribute most to the SZ contamination of very massive clusters at 90 and 148 GHz. These simulations are publicly available and should serve as a useful tool for microwave surveys to cross-check SZ cluster detection, power spectrum, and cross-correlation analyses.Comment: Sims are now public at http://lambda.gsfc.nasa.gov/toolbox/tb_cmbsim_ov.cfm; Expanded discussion of N-body sim and IGM; Version accepted by Ap

The Atacama Cosmology Telescope: A Measurement of the Cosmic Microwave Background Power Spectrum at 148 and 218 GHz from the 2008 Southern Survey

We present measurements of the cosmic microwave background (CMB) power spectrum made by the Atacama Cosmology Telescope at 148 GHz and 218 GHz, as well as the cross-frequency spectrum between the two channels. Our results clearly show the second through the seventh acoustic peaks in the CMB power spectrum. The measurements of these higher-order peaks provide an additional test of the {\Lambda}CDM cosmological model. At l > 3000, we detect power in excess of the primary anisotropy spectrum of the CMB. At lower multipoles 500 < l < 3000, we find evidence for gravitational lensing of the CMB in the power spectrum at the 2.8{\sigma} level. We also detect a low level of Galactic dust in our maps, which demonstrates that we can recover known faint, diffuse signals.Comment: 19 pages, 13 figures. Submitted to ApJ. This paper is a companion to Hajian et al. (2010) and Dunkley et al. (2010

The Atacama Cosmology Telescope: Data Characterization and Map Making

We present a description of the data reduction and mapmaking pipeline used for the 2008 observing season of the Atacama Cosmology Telescope (ACT). The data presented here at 148 GHz represent 12% of the 90 TB collected by ACT from 2007 to 2010. In 2008 we observed for 136 days, producing a total of 1423 hours of data (11 TB for the 148 GHz band only), with a daily average of 10.5 hours of observation. From these, 1085 hours were devoted to a 850 deg^2 stripe (11.2 hours by 9.1 deg) centered on a declination of -52.7 deg, while 175 hours were devoted to a 280 deg^2 stripe (4.5 hours by 4.8 deg) centered at the celestial equator. We discuss sources of statistical and systematic noise, calibration, telescope pointing, and data selection. Out of 1260 survey hours and 1024 detectors per array, 816 hours and 593 effective detectors remain after data selection for this frequency band, yielding a 38% survey efficiency. The total sensitivity in 2008, determined from the noise level between 5 Hz and 20 Hz in the time-ordered data stream (TOD), is 32 micro-Kelvin sqrt{s} in CMB units. Atmospheric brightness fluctuations constitute the main contaminant in the data and dominate the detector noise covariance at low frequencies in the TOD. The maps were made by solving the least-squares problem using the Preconditioned Conjugate Gradient method, incorporating the details of the detector and noise correlations. Cross-correlation with WMAP sky maps, as well as analysis from simulations, reveal that our maps are unbiased at multipoles ell > 300. This paper accompanies the public release of the 148 GHz southern stripe maps from 2008. The techniques described here will be applied to future maps and data releases.Comment: 20 pages, 18 figures, 6 tables, an ACT Collaboration pape

GAMA/G10-COSMOS/3D-HST: The 0<z<5 cosmic star-formation history, stellar- and dust-mass densities

We use the energy-balance code MAGPHYS to determine stellar and dust masses, and dust corrected star-formation rates for over 200,000 GAMA galaxies, 170,000 G10-COSMOS galaxies and 200,000 3D-HST galaxies. Our values agree well with previously reported measurements and constitute a representative and homogeneous dataset spanning a broad range in stellar mass (10^8---10^12 Msol), dust mass (10^6---10^9 Msol), and star-formation rates (0.01---100 Msol per yr), and over a broad redshift range (0.0 < z < 5.0). We combine these data to measure the cosmic star-formation history (CSFH), the stellar-mass density (SMD), and the dust-mass density (DMD) over a 12 Gyr timeline. The data mostly agree with previous estimates, where they exist, and provide a quasi-homogeneous dataset using consistent mass and star-formation estimators with consistent underlying assumptions over the full time range. As a consequence our formal errors are significantly reduced when compared to the historic literature. Integrating our cosmic star-formation history we precisely reproduce the stellar-mass density with an ISM replenishment factor of 0.50 +/- 0.07, consistent with our choice of Chabrier IMF plus some modest amount of stripped stellar mass. Exploring the cosmic dust density evolution, we find a gradual increase in dust density with lookback time. We build a simple phenomenological model from the CSFH to account for the dust mass evolution, and infer two key conclusions: (1) For every unit of stellar mass which is formed 0.0065---0.004 units of dust mass is also formed; (2) Over the history of the Universe approximately 90 to 95 per cent of all dust formed has been destroyed and/or ejected

Seeing Structure: Using Knowledge to Reconstruct and Illustrate Anatomy

Current medical imaging technology makes it possible to gather remarkably detailed three-dimensional data about an individual's anatomy. In domains ranging from education to clinical medicine, a common desire is the ability to examine selected structures from such volume datasets. This dissertation describes tools for performing the two key tasks in that process: reconstructing (or segmenting) specific structures from volume data and illustrating them in meaningful ways. On the reconstruction side, this work offers new, in-depth analysis of two previously proposed methods for using shape knowledge to guide image segmentation. The ideas are generalized to create a 3D shape model, which is used as part of a novel algorithm for semi-automatic segmentation of the brain. Unlike other methods, this approach offers intuitive user controls and explicitly addresses the removal of the skull and other surrounding structures. This method is incorporated into a working, interactive system for recording and studying functional data from the human brain. On the illustration side, several real-world situations are used to demonstrate how non-standard rendering methods can enhance the clarity of anatomical illustrations. The lessons learned from these examples lead to requirements for and a prototype of a medical illustration system