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Evaluation of deep learning segmentation for rapid, patient-specific CT organ dose estimation using an LBTE solver

Evaluation of deep learning segmentation for rapid, patient-...

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Conference on Medical Imaging - Physics of Medical Imaging

作者： Offe, Michael Fraley, David Adamson, Philip M. Principi, Sara Wang, Adam S. Jordan, Petr Schmidt, Taly Gilat Marquette Univ Dept Biomed Engn Milwaukee WI 53233 USA Med Coll Wisconsin Milwaukee WI 53226 USA Varian Med Syst Palo Alto CA USA Stanford Univ Dept Radiol Palo Alto CA 94304 USA

ISBN: (纸本)9781510633926

The overall goal of this work is to develop a rapid, accurate and fully automated software tool to estimate patient-specific organ doses from CT scans using a deterministic Linear Boltzmann Transport Equation (LBTE) solver (Acuros CTD) and deep-learning CT segmentation algorithms. This study evaluated the accuracy of deep learning segmentation for estimating organ dose from dose maps generated by Acuros. The study focused on pediatric CT due to increased radiation concerns and segmentation challenges for the pediatric population. Organs relevant to CT dosimetry were manually contoured by experts in 246 pediatric chest-abdomen-pelvis CT datasets to serve as ground truth. A fully convolutional network based on a modified V-net architecture was trained and tuned using 226 pediatric datasets ranging in age from 1 to 16 years. An additional twenty datasets were used for preliminary evaluation. The accuracy of organ dose estimates obtained from deep learning segmentation was evaluated relative to doses obtained from the ground truth contours. The deep learning segmentation algorithm resulted in low dose errors for all organs, with a mean absolute error across test patients of 1% or less and a maximum error of 3.5% for the heart. There was high similarity between deep learning and expert contours, with mean Dice coefficients across patients greater than or equal to 0.95. There was no correlation between organ dose error or Dice coefficient with the patient age. Based on statistical analysis of students paired T-test, there was no statistically significant difference between organ doses estimated using the deep learning contours as compared to the expert ground truth contours (p>0.2). Overall, the deep learning segmentation models applied to dose maps generated by the LBTE solver ( Acuros CTD) resulted in high organ dose accuracy. Additional evaluation is planned for more organ structures and patient datasets.

关键词： Computed tomography Error analysis Statistical analysis algorithm development Data modeling Heart Image segmentation Network architectures Software development

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Automated Global-Scale Detection and Characterization of Anthropogenic Activity using Multi-Source Satellite-Based Remote Sensing Imagery 13

Automated Global-Scale Detection and Characterization of Ant...

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Conference on Geospatial Informatics XIII

作者： Goldberg, Hirsh R. Ratto, Christopher R. Banerjee, Amit Kelbaugh, Michael T. Giglio, Mark Vermote, Eric F. Johns Hopkins Univ Appl Phys Lab Baltimore MD 21218 USA MITRE Corp Mclean VA USA NASA Goddard Space Flight Ctr Greenbelt MD USA

ISBN: (数字)9781510661653

ISBN: (纸本)9781510661646;9781510661653

Satellite-based remote sensing imagery is an effective means for detecting objects and structures in support of many applications. However, detecting the spatial and temporal bounds of a specific activity in satellite imagery is inherently more complex and research in this area is nascent. One reason for this is that describing an activity implies defining both spatial and temporal bounds and while activity is inherently continuous in nature, the geospatial (imagery) time series for any particular swath of ground provided by satellite imagery is relatively sparse and discrete in comparison. The IARPA Space-Based Machine Automated Recognition Technique (SMART)(1) program is the first large-scale research program to target advancing the state of the art for automatically detecting, characterizing, and monitoring large-scale anthropogenic activity in global, multi-spectral satellite imagery. The program has two primary research objectives: 1) the "harmonization" of multiple imagery sources and 2) automated reasoning at scale to detect, characterize, and monitor activities of interest. This paper provides details on the goals, dataset, metrics, and lessons learned of the IARPA SMART program. By releasing the annotated dataset, the program aims to foster additional research in this area by the community at large.

关键词： Satellites algorithm development Satellite imaging Earth observing sensors Object detection Evolutionary algorithms Machine learning

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Multivariate analysis of Eigenvalues and Eigenvectors in tensor based morphometry 10

Multivariate analysis of Eigenvalues and Eigenvectors in ten...

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10th International Symposium on Medical Information Processing and Analysis

作者： Rajagopalan, Vidya Schwartzman, Armin Hua, Xue Leow, Alex Thompson, Paul Lepore, Natasha Childrens Hosp Los Angeles Dept Radiol Los Angeles CA 90025 USA Univ Southern Calif Imaging Genet Ctr Inst Neuroimaging & Informat Los Angeles CA USA North Carolina State Univ Dept Stat Raleigh NC 27695 USA Univ Illinois Dept Psychiat Chicago IL 60607 USA Univ Illinois Dept Bioengn Chicago IL 60607 USA

ISBN: (纸本)9781628413625

We develop a new algorithm to compute voxel-wise shape differences in tensor-based morphometry (TBM). As in standard TBM, we non-linearly register brain Ti-weighed MRI data from a patient and control group to a template, and compute the Jacobian of the deformation fields. In standard TBM, the determinants of the Jacobian matrix at each voxel are statistically compared between the two groups. More recently, a multivariate extension of the statistical analysis involving the deformation tensors derived from the Jacobian matrices has been shown to improve statistical detection power.(7) However, multivariate methods comprising large numbers of variables are computationally intensive and may be subject to noise. In addition, the anatomical interpretation of results is sometimes difficult. Here instead, we analyze the eigenvalues and the eigenvectors of the Jacobian matrices. Our method is validated on brain MRI data from Alzheimer's patients and healthy elderly controls from the Alzheimer's Disease Neuro Imaging Database.

关键词： Statistical analysis Brain Matrices Neuroimaging Magnetic resonance imaging Alzheimer's disease Tissues Databases Diffusion tensor imaging algorithm development

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PULSE SHAPE-DISCRIMINATION FOR BACKGROUND REJECTION IN GERMANIUM GAMMA-RAY DETECTORS

PULSE SHAPE-DISCRIMINATION FOR BACKGROUND REJECTION IN GERMA...

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CONF ON EUV, X-RAY, AND GAMMA-RAY INSTRUMENTATION FOR ASTRONOMY AND ATOMIC PHYSICS

作者： FEFFER, PT SMITH, DM CAMPBELL, RD PRIMBSCH, JH LIN, RP LUKE, PN MADDEN, NW PEHL, RH MATTESON, JL University of California Berkeley (United States) University of California (United States)

ISBN: (纸本)0819401951

We report on the development of a pulse shape discrimination (PSD) technique to reject the B-decay background resulting from activation of germanium (Ge) gamma-ray detectors by cosmic ray secondaries. These B-decays are a major source of background at 0.2-2 MeV energies in well shielded Ge detector systems. The technique exploits the difference between the detected current pulse shapes of single- and multiple-site energy depositions within the detector: B-decays are primarily single-site events while photons at these energies typically Compton scatter before being photoelectrically absorbed to produce multiple-site events. algorithms have been developed to distinguish between single- and multiple-site pulse shapes. Depending upon the amount of background due to sources other than B-decay, PSD can more than double the detector sensitivity. In addition, we report on tests of PSD by laboratory activation of a detector with a fast neutron source, and on the first direct measurement of the B-decay background at balloon float altitude using a Ge detector with PSD.

关键词： Sensors Germanium Photons Gamma radiation X-ray astronomy Astronomy Atomic, molecular, and optical physics Extreme ultraviolet Gamma-ray astronomy algorithm development

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Polarization-Sensitive Digital Dermoscopy For Image Processing-Assisted Evaluation Of Atypical Nevi: Towards Step-Wise Detection Of Melanoma

Polarization-Sensitive Digital Dermoscopy For Image Processi...

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Conference on Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues IX

作者： Yu, Lauren S. Joseph, Anika O. N. R. Lindsley, Erik H. Farkas, Daniel L. Windward Sch Los Angeles CA USA Univ Southern Calif Dept Biomed Sci Los Angeles CA USA Spectral Mol Imaging Beverly Hills CA USA

ISBN: (纸本)9780819484390

We have taken a three-pronged approach to improving the current standard of melanoma detection: (a) we are developing a new hyperspectral imaging-based medical device aimed at non-invasively detecting melanoma (b) we used a commercially available hand-held microscope with polarization control as a dermoscope, to begin establishing an inexpensive, portable imaging capability that could help assess the risk of a particular lesion (pigmented nevus) harboring melanoma (c) we created an updated ABCD algorithm and user interface software that more accurately generates a single risk number (Total Dermoscopy Score), for allowing a trained clinician to better assess the need for seeing the patient whose internet-uploaded nevus images they are evaluating. The hyperspectral instrument (a) is discussed elsewhere, and we focus here on (b) and (c), in the hope of increasing melanoma awareness and early detection.

关键词： Melanoma Image processing Image analysis Digital image processing Fractal analysis Detection and tracking algorithms Skin algorithm development Hyperspectral imaging Medical imaging

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UNSUPERVISED TARGET DETECTION IN A SINGLE IR IMAGE FRAME 14

UNSUPERVISED TARGET DETECTION IN A SINGLE IR IMAGE FRAME

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CONF ON APPLICATIONS OF DIGITAL IMAGE PROCESSING 14

作者： ZHOU, YT HNC Inc. (United States)

ISBN: (纸本)0819406953

Detecting targets from infrared (IR) images in the absence of a priori information is a very difficult task. In this paper, we present an unsupervised detection algorithm based on Gabor functions for detecting targets from a single IR image frame. The only explicit assumption made is that the targets can be considered a rectangle. The algorithm consists of three steps. First, it locates potential targets based on a rectangle matching pattern by using low resolution Gabor functions which resist noise and background clutter effects. Then it removes false targets and eliminates redundant target points based on a similarity measure. These two steps mimic human vision processing but are different from Zeevi's Foveating Vision System. Finally, it uses both low and high resolution Gabor functions to verify target existence. This algorithm has been successfully tested on several IR images that contain multiple examples of military vehicles and aircraft with different size and brightness in various background scenes and orientations.

关键词： Target detection Detection and tracking algorithms Evolutionary algorithms Infrared imaging Digital image processing algorithm development Image processing algorithms and systems Infrared detectors Human vision and color perception Image segmentation

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NEURAL NETWORKS FOR PROCESSING DATA FROM MULTIPLE REDUNDANT SENSORS FOR MINE SYSTEMS MANAGEMENT, OPERATION, MAINTENANCE, AND CONTROL 6

NEURAL NETWORKS FOR PROCESSING DATA FROM MULTIPLE REDUNDANT ...

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CONF ON SENSOR FUSION 6

作者： GORDON, A CHANG, H KING, R Colorado School of Mines (United States)

ISBN: (纸本)0819413240

We have developed a neural-network approach to classifying signals by fusing information from multiple sensors. During the past three years, we have developed concepts and algorithms for an intelligent decision support system (IDSS) for mine managers. The goal of the IDSS is to detect the activities of machines in an underground coal mine and to produce management reports similar to traditional industrial engineering time studies. The data we operate on is power usage of the various machines taken every 50 milliseconds. Currently we are working with data from three machines which interact with each other: a continuous miner and two shuttle cars. Detection of events was first done using numerical techniques to arrive at locally best guesses and rule-based techniques to fuse the information from the different machines. Our current research involves dynamic recurrent neural networks (a variation of recurrent cascade correlation) which replace the numerical and rule-based techniques. Our current neural networks can accurately label approximately 90% of the machine events in the training set and approximately 70% in new data sets. Neural network techniques are able to adjust to the dynamic mine environment much better than the previous algorithms, consequently, the neural network approach is more acceptable in the applications environment.

关键词： Neural networks Mining Sensor fusion Sensors Land mines Signal processing algorithm development Control systems Decision support systems Intelligence systems

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High-speed parameter estimation algorithms for nonlinear smart materials

High-speed parameter estimation algorithms for nonlinear sma...

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Conference on Modeling, Signal Processing, and Control for Smart Structures 2007

作者： Ernstberger, Jon M. Smith, Ralph C. North Carolina State Univ Dept Math Ctr Res Sci Computat Box 8205 Raleigh NC 27695 USA

ISBN: (纸本)9780819466440

A fundamental step in the model construction for ferroelectric, ferromagnetic, and ferroelastic materials is the estimation or identification of material parameters given measurements of the material response. Moreover, actuator and/or material properties may be a function of operating conditions which can necessitate the re-estimation of parameters if conditions change significantly. In this paper, we focus on the development of highly robust and efficient identification algorithms for use in industrial, aeronautic and aerospace applications. Following a discussion of present and future applications, we summarize the homogenized energy model used to characterize hysteresis and constitutive nonlinearities in these compounds. We next discuss the parameter estimation problem and detail algorithms used to speed implementation. The validity of the framework is illustrated through comparison with experimental data.

关键词： Data modeling Actuators Smart materials Genetic algorithms algorithm development algorithms Ferromagnetics Aerospace engineering Magnetism Transducers

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ASSESSING THE PERFORMANCE OF MULTISENSOR FUSION SYSTEMS

ASSESSING THE PERFORMANCE OF MULTISENSOR FUSION SYSTEMS

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SENSOR FUSION CONF : CONTROL PARADIGMS AND DATA STRUCTURES

作者： LLINAS, J Calspan Corp. (United States)

ISBN: (纸本)0819407488

In recent years, numerous prototypical systems have been developed for multisensor data fusion. A typical data fusion process operates on sensor parametric data (e.g., data related to target position or attribute data) in order to develop an order of battle, provide an evaluation of tactical situations, or assess tactical threats. This model, developed by the Data Fusion Sub- panel (DFS) of the Joint Directors of Laboratories, partitions fusion processing into four conceptual levels. Ancillary functions in a fusion system include the human computer interface, data base management, source-preprocessing functions, and communications. Military applications for data fusion span a broad range including fusion of data on board a single platform for identifying other platforms (e.g., identification--friend or foe--neutral systems), threat warning systems, situation assessment, and threat assessment systems. Large scale systems such as the All-Source Analysis System (ASAS) or the Joint Surveillance, Targeting, and Reconnaissance System (JSTARS) provide for direction, coordination, and fusion of both ground-based and airborne sensors to aid in the effective management of a ground based battlefield environment. Such systems have become ever more sophisticated. Indeed, many of the prototypical systems utilize advanced identification techniques such as knowledge-based or expert systems. Dempster-Shafer interface techniques, adaptive neural networks, and sophisticated tracking algorithms. While much research is being performed to develop and apply new algorithms and techniques, little work has been performed to determine how well such methods work or to compare alternative methods against a common problem. The issues of system performance and system effectiveness are keys to establishing how well an algorithm, technique, or collection of techniques perform, and then the extent to which these techniques may be used to achieve success on an operational mission.

关键词： Data fusion Sensors Data modeling Telecommunications Sensor fusion Detection and tracking algorithms Target detection Data acquisition Computing systems algorithm development

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IDENTIFICATION OF PASSIVE MILLIMETER-WAVE IMAGES USING NEURAL NETWORKS 4

IDENTIFICATION OF PASSIVE MILLIMETER-WAVE IMAGES USING NEURA...

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4th International Conference on Applications of Artificial Neural Networks

作者： SUNDSTROM, B MIN, K USAF WRIGHT LABEGLIN AFBFL 32542

ISBN: (纸本)0819412015

Recent developments in passive millimeter-wave imaging technology are remarkable. Images of objects obtained through clouds and fog are almost indistinguishable from similar scenes taken under clear conditions. Of particular interest is the ability to image metal targets beneath camouflage, tents, polymers, wooden shelters, and certain levels of ceramic materials. A brief description of this emerging technology will be followed by several convincing examples of images to support the claims made above. Once image formation is complete, the technique of identifying objects in the image using neural networks is similar to the schemes utilized in previous Wright Laboratory Armament directorate implementations of Automatic Target Identification work for electro-optical and infrared images.

关键词： Neural networks Extremely high frequency Passive millimeter wave imaging Sensors Artificial neural networks Infrared imaging algorithm development Staring arrays Imaging systems Visible radiation

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