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Title: Image-based informatics for Preclinical Biomedical Research

Abstract

In 2006, the New England Journal of Medicine selected medical imaging as one of the eleven most important innovations of the past 1,000 years, primarily due to its ability to allow physicians and researchers to visualize the very nature of disease. As a result of the broad-based adoption of micro imaging technologies, preclinical researchers today are generating terabytes of image data from both anatomic and functional imaging modes. In this paper we describe our early research to apply content-based image retrieval to index and manage large image libraries generated in the study of amyloid disease in mice. Amyloidosis is associated with diseases such as Alzheimer's, type 2 diabetes, and myeloma. In particular, we will focus on results to date in the area of small animal organ segmentation and description for CT, SPECT, and PET modes and present a small set of preliminary retrieval results for a specific disease state in kidney CT cross-sections.

Authors:
 [1];  [1];  [1];  [1];  [1];  [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE; Work for Others (WFO)
OSTI Identifier:
966072
DOE Contract Number:
DE-AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: International Symposium on Visual Computing, Lake Tahoe, NV, USA, 20061106, 20061108
Country of Publication:
United States
Language:
English

Citation Formats

Tobin Jr, Kenneth William, Aykac, Deniz, Price, Jeffery R, Gregor, Jens, Wall, Jonathan, and Muthusamy Govindasamy, Vijaya Priya. Image-based informatics for Preclinical Biomedical Research. United States: N. p., 2006. Web.
Tobin Jr, Kenneth William, Aykac, Deniz, Price, Jeffery R, Gregor, Jens, Wall, Jonathan, & Muthusamy Govindasamy, Vijaya Priya. Image-based informatics for Preclinical Biomedical Research. United States.
Tobin Jr, Kenneth William, Aykac, Deniz, Price, Jeffery R, Gregor, Jens, Wall, Jonathan, and Muthusamy Govindasamy, Vijaya Priya. Sun . "Image-based informatics for Preclinical Biomedical Research". United States. doi:.
@article{osti_966072,
title = {Image-based informatics for Preclinical Biomedical Research},
author = {Tobin Jr, Kenneth William and Aykac, Deniz and Price, Jeffery R and Gregor, Jens and Wall, Jonathan and Muthusamy Govindasamy, Vijaya Priya},
abstractNote = {In 2006, the New England Journal of Medicine selected medical imaging as one of the eleven most important innovations of the past 1,000 years, primarily due to its ability to allow physicians and researchers to visualize the very nature of disease. As a result of the broad-based adoption of micro imaging technologies, preclinical researchers today are generating terabytes of image data from both anatomic and functional imaging modes. In this paper we describe our early research to apply content-based image retrieval to index and manage large image libraries generated in the study of amyloid disease in mice. Amyloidosis is associated with diseases such as Alzheimer's, type 2 diabetes, and myeloma. In particular, we will focus on results to date in the area of small animal organ segmentation and description for CT, SPECT, and PET modes and present a small set of preliminary retrieval results for a specific disease state in kidney CT cross-sections.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Jan 01 00:00:00 EST 2006},
month = {Sun Jan 01 00:00:00 EST 2006}
}

Conference:
Other availability
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  • In 2006, the New England Journal of Medicine selected medical imaging as one of the eleven most important innovations of the past 1,000 years, primarily due to its ability to allow physicians and researchers to visualize the very nature of disease. As a result of the broad-based adoption of micro imaging technologies, preclinical researchers today are generating terabytes of image data from both anatomic and functional imaging modes. In this paper we describe our early research to apply content-based image retrieval to index and manage large image libraries generated in the study of amyloid disease in mice. Amyloidosis is associatedmore » with diseases such as Alzheimer's, type 2 diabetes, chronic inflammation and myeloma. In particular, we will focus on results to date in the area of small animal organ segmentation and description for CT, SPECT, and PET modes and present a small set of preliminary retrieval results for a specific disease state in kidney CT crosssections.« less
  • Biomedical informatics refers to the study of the application of computational and statistical algorithms, data structures, and methods to improve communication, understanding and management of biomedical information. Our objective through this chapter is to describe and demonstrate our research in the use of biomedical image databases - in both preclinical and clinical settings - to classify, predict, research, diagnose, and otherwise learn from the informational content encapsulated in historical image repositories. This will be accomplished by detailing our approach of describing image content in a Bayesian probabilistic framework to achieve learning from retrieved populations of similar images. We will usemore » specific examples from two biomedical applications to describe anatomic segmentation, statistical feature generation and indexing, efficient retrieval architectures, and predictive results.« less
  • Purpose: In our previous study a preclinical multi-modality quality assurance (QA) phantom that contains five tumor-simulating test objects with 2, 4, 7, 10 and 14 mm diameters was developed for accurate tumor size measurement by researchers during cancer drug development and testing. This study analyzed the errors during tumor volume measurement from preclinical magnetic resonance (MR), micro-computed tomography (micro- CT) and ultrasound (US) images acquired in a rodent tumor model using the preclinical multi-modality QA phantom. Methods: Using preclinical 7-Tesla MR, US and micro-CT scanners, images were acquired of subcutaneous SCC4 tumor xenografts in nude rats (3–4 rats per group;more » 5 groups) along with the QA phantom using the same imaging protocols. After tumors were excised, in-air micro-CT imaging was performed to determine reference tumor volume. Volumes measured for the rat tumors and phantom test objects were calculated using formula V = (π/6)*a*b*c where a, b and c are the maximum diameters in three perpendicular dimensions determined by the three imaging modalities. Then linear regression analysis was performed to compare image-based tumor volumes with the reference tumor volume and known test object volume for the rats and the phantom respectively. Results: The slopes of regression lines for in-vivo tumor volumes measured by three imaging modalities were 1.021, 1.101 and 0.862 for MRI, micro-CT and US respectively. For phantom, the slopes were 0.9485, 0.9971 and 0.9734 for MRI, micro-CT and US respectively. Conclusion: For both animal and phantom studies, random and systematic errors were observed. Random errors were observer-dependent and systematic errors were mainly due to selected imaging protocols and/or measurement method. In the animal study, there were additional systematic errors attributed to ellipsoidal assumption for tumor shape. The systematic errors measured using the QA phantom need to be taken into account to reduce measurement errors during the animal study.« less
  • This paper addresses cognitive implications and research needs surrounding the problem of cyber friendly fire (FF). We define cyber FF as intentional offensive or defensive cyber/electronic actions intended to protect cyber systems against enemy forces or to attack enemy cyber systems, which unintention-ally harms the mission effectiveness of friendly or neutral forces. Just as with combat friendly fire, maintaining situation awareness (SA) is paramount to avoiding cyber FF incidents. Cyber SA concerns knowledge of a system’s topology (connectedness and relationships of the nodes in a system), and critical knowledge elements such as the characteristics and vulnerabilities of the components thatmore » comprise the system and its nodes, the nature of the activities or work performed, and the available defensive and offensive countermeasures that may be applied to thwart network attacks. Mitigation strategies to combat cyber FF— including both training concepts and suggestions for decision aids and visualization approaches—are discussed.« less
  • Radioisotope tracing has long been an important means of determining various parameters of biological significance. AMS is a technique well suited for tracing {sup 14}C and other isotopes ({sup 41}Ca, {sup 26}AI, {sup 3}H, {sup 36}Cl, {sup 129}I) through biological systems at levels between 10{sup 3}-10{sup 6}-fold lower than decay counting can achieve. The author has been applying accelerator mass spectrometry (AMS), to tracing isotopically-labeled chemicals in toxicological and pharmacological research for the past 7 years. In toxicology and pharmacology, AMS can play a major role in determining the consequences of exposure to chemicals at very low doses, in understandingmore » mechanisms of action, and in assessing the dosimetric relationships between high-dose animal studies and the very low dose exposures more typical for humans. In addition, AMS is being used as an isotope detector in combination with biochemical separation methods giving attomole sensitivity for following specific molecules. AMS can also be applied in human subjects studies. The ability to detect specific molecules at attomoles levels will have a major impact in defining the risks chemical pose to human health. However, widespread use of this technique by the biomedical research community will depend on the availability of small, inexpensive, instruments.« less