Visible Human, Know Thyself:

The Digital Anatomist Structural Abstraction


Cornelius Rosse, M.D., D.Sc., José L. V. Mejino, M.D., Linda G. Shapiro, Ph.D.,

James F. Brinkley, M.D., Ph.D.


Structural Informatics Group, Departments of Biological Structure and Computer Science, University of Washington, Seattle, WA



The Visible Human data sets have stimulated a great deal of activity in the graphical representation of anatomy. A major challenge is to enhance this resource of image-based information with knowledge of its own structure. There is a need for a symbolic model of the structural organization of the human body, which could invest with meaning the graphical information extractable from the clusters of voxels and their geometric coordinates that make up the Visible Human data sets. The objective of this communication is to examine the elements of structural information such a symbolic model should encompass, and to assess the extent to which the Anatomical Structural Abstraction (ASA) of the Digital Anatomist Foundational Model of Anatomy (Fm)1 meets this objective.


Elements of Structural Information. In a biological or anatomical context, the term structure is associated with two distinct concepts (meanings): 1. a material object generated as a result of coordinated gene expression, which necessarily consists of parts (e.g., hemoglobin molecule, cell, heart, human body); and 2. the manner of organization or interrelation of the parts that constitute a structure specified by the first definition (i.e., the structure of a structure). Both definitions emphasize the critical need for declaring the principles according to which units of organization can be defined in order to be able to state what is ‘whole’ and what is ‘part’. Specifying the manner in which parts interrelate must satisfy two requirements: 1. to determine the kinds of parts of which various structures may be constituted; and 2. to state the manner of spatial organization of parts by describing their boundaries, continuities and attachments, as well as their location, orientation and spatial adjacencies in terms of qualitative coordinates (in addition to the quantitative geometric coordinates, which are embedded in the Visible Human data sets).


The Anatomical Structural Abstraction. The ASA is one of the four components of the Digital Anatomist Foundational Model of Anatomy (Fm):


Fm = (Ao, ASA, ATA, Mk)        (1)


The Fm is an abstraction that, in accord with declared principles, describes the physical organization of the material objects and 3D spaces that constitute an idealized human body1. The current model is limited to the static state and excludes anatomical structures smaller than the cell.  Its backbone is the Anatomy ontology (Ao), which assigns anatomical entities to classes according to defining attributes they share with one another and by which they may be distinguished from one another2. ASA, the subject of this communication, is described after other Fm components. ATA, the Anatomical Transformation Abstraction describes time-dependent morphological transformations of anatomical structures during the human life cycle. Mk, Metaknowledge, comprises the principles, definitions and rules according to which relationships are represented in the other three components of Fm.


The ASA captures the information that is sufficient and necessary for describing the structure of any physical object or space that constitutes the body, as well as that of the entire human body itself. The ASA of the entire body may be conceived as a composite of all ASAs of anatomical structures and anatomical spaces that are represented in Ao. The Anatomical Structural Abstraction is distinct from other approaches that have been proposed for the symbolic description of anatomical spatial relationships, in that it is not limited to object recognition in medical


images, it generalizes to all parts of the body and

it accommodates all relationships that are necessary for describing the 3D structure of the body. The ASA consists of several components:


ASA = (So, Pn, Bn, SAn)                 (2)


where:    So   = Spatial object ontology

                Pn   = Part-of network

                Bn   = Boundary network

                SAn = Spatial association network


The So provides an additional axis for classifying the thousands of anatomical concepts in Ao according to their spatial dimensions and shape, and thereby systematizes the description of their spatial relationships. For instance, through the rules entered in Mk, part-whole relationships in Pn are restricted to spatial objects of the same dimension. For example, the relationship ‘Right atrium’ -has parts- ‘Cavity of right atrium’, ‘Wall of right atrium’ is sanctioned because in So all these concepts are classified as Volume (3D object). On the other hand, ‘Surface of right atrium’ cannot be modeled as part of ‘Right atrium’, because it is a 2D object. The correct relationship is specified by Bn: ‘Right atrium’ -bounded by- ‘Surface of right atrium’, because the latter is classified as a spatial object of one lower dimension than the atrium. Pn also incorporates an ontology of part-whole relationships, which are sanctioned according to the assignment of a concept to classes of Ao and So. For instance the relationship -has lobe- is sanctioned if the ‘whole’ is classified as a “Parenchymatous organ’ in Ao and a ‘Cone’ ‘Semicone’ or ‘Polyhedron’ in So.


The Spatial Association Network (SAn) itself encompasses subnetworks of various relationships:


                SAn  =  (Ctn, Atn, Ln, On, Ajn)        (3)


where:    Ctn   = Continuity network

Atn   = Attachment network

Ln    = Location network

                On    = Orientation network

                Ajn   = Adjacency network 


The latter three subnets of the SAn make use of traditional anatomical descriptors of orientation and location (anterior, posterior, etc.) as qualitative coordinates in terms of the shape of the spatial object that is being described.


We began to represent the Fm as an extension and enhancement of the UMLS semantic network. A semantic net, however, is not sufficiently expressive for modeling the multiple relationships that constitute the ASA. Therefore, we are in the process of migrating the model to Protégé, a frame-based system designed for accommodating multiple relationships, supporting inheritance and correlating several ontologies3. The Ao, So and Pn components of Fm, along with relevant elements of Mk have been implemented for macroscopic anatomy. Work is in progress on the structural networks of the ASA. The Visible Human geometric data sets serve for verifying spatial relationships we model and also for validating the ASA schemes that we develop. This work would be greatly facilitated by the availability of 3D graphical models for all anatomical structures that can be segmented from the volumetric data.  


It is our contention that the Digital Anatomist Foundational Model as a whole, and its Anatomical Structural Abstraction in particular, will furnish the formal representation of knowledge that will provide for the intelligent navigation of the Visible Human data sets. Through appropriately designed interfaces, the Fm will be instrumental in revealing the structure of the human body not only to experts but also to any user who has a need for interacting with the Visible Human.


Supported by National Library of Medicine contract, LM 83510 and grants LM 06822 and LM 06316.  




1.Rosse C,  Shapiro LG, and  Brinkley JF.  The Digital Anatomist Foundational Model: principles for defining and structuring its concept domain.  J Am Med Inform Assoc Proc AMIA’98 Annual Symposium 1998;820-824.


2.Rosse C, Mejino JL, Modayur BR, Jakobovits R,       Hinshaw KP, Brinkley JF. Motivation and organizational principles for anatomical knowledge representation: the Digital Anatomist Symbolic Knowledge Base. J Am Med Inform Assoc 1998;5:17-40.


3.Musen MA, Gennari JH, Eriksson H, Tu SW, Puerta AR. PROTÉGÉ II:computer support for development of intelligent systems from libraries of components. MEDINFO95, The eighth World Congress of Medical Informatics, Vancouver, B.C. Canada, 1995;766-770.