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Dynamic Scene Generation of both Anatomy and Information: Towards Using Structure as an Illustrative Framework. Wayne Warren University of Washington, Department of Biomedical and Health Informatics Seattle, WA. Introduction

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  1. Dynamic Scene Generation of both Anatomy and Information: Towards Using Structure as an Illustrative Framework Wayne Warren University of Washington, Department of Biomedical and Health Informatics Seattle, WA Introduction Three-dimensional visualization of human anatomy is an effective way of describing appearances and spatial relationships within the human body. It is also a potentially powerful method of integrating and describing biomedical research data which can be related to anatomical entities. Toward this end, a computer-based dynamic scene generation system has been created which constructs interactive three-dimensional scenes of human anatomy. The Dynamic Scene Generator allows the construction of scenes built from a collection of user-selected anatomical structures. An exploratory goal of this system is to serve as the framework for the visualization of biomedical data which relates directly to the scene generator’s rendered structures. Mapping microarray expression data of various genes onto a physical map of human anatomy is one such application. Querying microarray gene expression data from human tissue samples should be able to yield an identity, expression level, and biological source (anatomical structure), if that data is archived according to appropriate standards. Using this data to help illustrate an anatomical scene may give users an appreciation for patterns not easily seen without the help of such visualization. Data Access and Formats As genomics and high-throughput microarray methods have continued to evolve, the amount of microarray data being produced by the research community continues to increase. Some of the data produced is made available to the public through accessible microarray-specific databases. Three public microarray databases will be the main source of gene expression information to illustrate our scenes. GEO, or Gene Expression Omnibus was one of the first public repositories for submission, curation, and retrieval of microarray data. Becoming operational in July 2000, GEO currently has expression data on more than 8000 samples. GEO currently uses a data communication format called SOFT, or Simple Omnibus Format in Text, its implementation being an ASCII text file with simple data descriptions and space-delimited values. Another standard microarray data representation format, MAGE-ML, or Microarray Gene Expression Markup language, was originated by the MGED working group. It is the product of fusing a gene expression-specific schema, or document type definition, with MAGE-OM, a UML-style object model describing microarray experimental data. Databases utilizing this representation method require less file-parsing work from their users, as Java classes for the extraction of information are openly available. The Stanford Microarray Database, or SMD, is by far one of the largest repositories of microarray data in the world. With more than 140 unique microarray studies available to the public, and 62 of those studies representing work on humans, SMD is a powerful gene expression resource. SMD uses Excel as a standard file format for the download of the complete experimental data from its web GUI. Literature regarding the development of SMD specifically states that efforts to add MIAME compliance, as well as support for MAGE-ML output, are currently underway. ArrayExpress, supported by the European Bioinformatics Institute (EBI), houses a number microarray studies that could be relevant to our work. An encouraging fact known of ArrayExpress and the EBI is that the MAGE data representation framework has already been implemented and data are now available in this format. Figure 2: A scene constructed in the Dynamic Scene Generator showing the heart and some of its surrounding anatomy The Dynamic Scene Generation The Dynamic Scene Generator Client The Dynamic Scene Generator Client is responsible for communicating with both the FMA and with the Brodel Server. Using information gather from both sources, the client is able to assemble three-dimensional anatomical scenes using detailed anatomical knowledge. The user builds the scene through interacting with a GUI which allows the adding, removing, and highlighting of specific anatomical structures by name or by hierarchical reference. Written in Java 1.4, the DSG will eventually as a web-based application, most likely through Sun’s Web Start. The functionality of the DSG requires the implementation of a technology to allow interaction with the VRML scene. The EAI, or the External Authoring Interface, is a standard API adopted by the VRML community which allows for communication between external Java code and the VRML scene. Rather than rely on the various third-party VRML browsers which implement variations of EAI, it was decided that XJ3D be used as the primary method of rendering a VRML scene. XJ3D is a project of the web 3D consortium to create a visualization toolkit which renders both X3D and VRML exclusively with Java. Figure 1: A schematic describing the components required for dynamic generation of scenes of anatomy Anatomical Knowledge Source The Foundational Model of Anatomy The Foundational Model of Anatomy (FMA), is a knowledgebase describing the structure and relationships between components of anatomy. Implemented in a frame-based system, Protégé, the FMA has approximately 70,000 concepts and over 100,000 terms. It is capable of answering questions such as “what entities serves as the venous drainage of the heart?” or “what innervates the deltoid muscle?”. OQAFMA Like many knowledge bases built in Protégé, the FMA can be queried through the use of SQL through Protégé’s standard API. While the protégé API originally satisfied users’ requirements for querying the FMA, it has become clear in recent years that an agent which supported more complex queries was needed. OQAFMA is a robust querying agent to the FMA which uses STRUQL (Structure Query Language) as its query language. OQAFMA allows more flexible use of the FMA by allowing complex operations such as queries with transitive closures (such as “give me the heart and ALL of its parts?” (parts of parts of parts…). Towards Using Anatomy to Illustrate Biomedical Research Data Generation of complex anatomical scenes has many uses in biomedical education and illustration. However, such scenes can be used as an illustrative framework for data visualization to aid research as well. Once such application is the visualization of gene expression associated with cells located in discreet anatomical structures. Figure 4: A schematic of the future DSG client may integrate microarray data sources with anatomical information and image sources. References Albright, E. M. (2000) Dynamic scene generation and software parallel rendering of anatomical structures. MS thesis, Electrical Engineering, University of Washington. Brinkley, J. F. and Wong, B. A. and Hinshaw, K. P. and Rosse, C. (1999) Design of an Anatomy Information System. Computer Graphics and Applications 19(3):38-48. Invited paper. Brinkley, J. F. and Albright, E. M. and Kim, S. and Mejino, J. L. V. and Shapiro, L. G. and Rosse, C. (2000) Visible Human, Construct Thyself: The Digital Anatomist Dynamic Scene Generator. In Proceedings, Visible Human Project Conference 2000, pages 27-28. Gollub J, Ball CA, Binkley G, Demeter J, Finkelstein DB, Hebert JM, Hernandez-Boussard T, Jin H, Kaloper M, Matese JC, Schroeder M, Brown PO, Botstein D, Sherlock G. The Stanford Microarray Database: data access and quality assessment tools. Nucleic Acids Res 2003 Jan 1;31(1):94-6. Hinshaw, K. P. (2000) Seeing Structure: Using Knowledge to Reconstruct and Illustrate Anatomy . PhD dissertation, Computer Science and Engineering, University of Washington. Mork, P. and Brinkley, J. F. and Rosse, C. (2003) OQAFMA Querying Agent for the Foundational Model of Anatomy: a Prototype for Providing Flexible and Efficient Access to Large Semantic Networks. Journal of Biomedical Informatics. In press. Rosse, C. and Shapiro, L. G. and Brinkley, J. F. (1998) The Digital Anatomist Foundational Model: Principles for Defining and Structuring Its Concept Domain. In Proceedings, American Medical Informatics Association Fall Symposium, pages 820-824. Wong, B. A. and Rosse, C. and Brinkley, J. F. (1999) Semi-Automatic Scene Generation Using the Digital Anatomist Foundational Model. In Proceedings, American Medical Informatics Association Fall Symposium, pages 637-641. Model Information Source The Brodel Server The Brodel Server, named for Max Brodel of medical illustration fame, serves as a “light” web service which serves as a communication portal to the three-dimensional model database, a session manager for users manipulating worlds, and a file manipulation engine. The Brodel server accepts commands soliciting information regarding models such as requests for vertices and faces. Its file manipulation engines allow both the client and the three dimensional model database to use several graphics file formats, since all graphics information is fed into intermediate abstract data types. The Three-Dimensional Model Database Still in development, the Three-Dimensional Model Database is responsible for storing and validating information required to located the models needed for the scene. Figure 3: A scene constructed in the Dynamic Scene Generator, showing a potential example of illustrating gene expression in some of the intercostal veins and arteries Brodel/ Model-DB Model Query XML DSG Client FMA/ OQAFMA Gene expression data SMD FMA Query Array-Express Gene Expression Query Scene data GEO 3D Scene

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