DESCRIPTION (adapted from the applicant's abstract):
The systematic combination of anatomy and nomenclature is an essential
component of all computer processes that seek to visualize, relate,
manipulate, and compare three-dimensional human anatomic structures.
Structure names must be elemental, categorized and associated with standard
comparison images in order to: create computer visualizations at multiple
levels of detail; associate structures spatially and symbolically; assign
anatomic units their physical characteristics; and categorize new patient-
derived anatomic data. This project will demonstrate that the Systematic
Combination of Anatomy and Nomenclature (SCAN) can enable semi-automatic
identification and segmentation of structures in radiological data.
The first long-term goal of this project is to relate three-dimensional
anatomic knowledge in the Visible Human Data (VHD, a full digital description
of the human being) to the symbolic anatomic knowledge in the Systemized
Nomenclature of Human and Veterinary Medicine (SNOMED, a data table relating
terms to codes), which is a component of the Unified Medical Language System
(UMLS, a relational database). This project will identify elemental gross
anatomic structures within the nomenclature of SNOMED and relate them to
registered, segmented slice image sets and high quality Virtual Reality
Modeling Language (VRML) surface models derived from VHD.
The second long-term goal of this project is to link the standard anatomic
knowledge to new three-dimensional radiological data for any patient. This
project will generate a semi-automated process for combination of patient-
specific anatomic data and nomenclature using mutual information for automatic
multimodality image fusion (MIAMI Fuse, a process which integrates three-
dimensional images of different modalities into a single image set) to link
new data to standardized data and subsequently to nomenclature.
By integrating standardized three-dimensional and symbolic anatomy, coupled
with semi-automated fusion of new radiological data to these standards, SCAN
will provide the foundation for widespread advances in biomedical
visualization, simulation, medical education, diagnostics, and treatment