Design of an Ontology-Enhanced Anatomy Labeler

In this paper, we present a formal theory for symbolically modeling the spatial relationships that exist between gross-level anatomical structures in the human body. We develop these theories with the goal of computer-based inference. The formal theories are used for building models, which can be applied on graph representations of medical images. We describe an end-to-end design for inferring (labeling) parts of the anatomy from an unlabeled three-dimensional data set. Given a finite set of labels (corresponding to anatomical structures from a taxonomy), we probabilistically assign one label to each node in an anatomical graph. The paradigm we present is generalizable and can help bridge the gap between purely informational ontologies and ontologies for intelligent agents. 1 Motivation and Background The data from most current medical imaging modalities such as Computerized Tomography (CT), ultrasound, Positive Emission Tomography (PET) etc., are stored in digital format, yet image reading/analysis is still a very manual process with little or no computer intelligence involved. Medical image reading by humans can be a slow and tedious process, prone to human errors while lacking objective means of quantifying and assessing the reader’s skills. To overcome this problem in clinical settings, multiple readers are required to review the same data sets, to compensate for any errors that might arise from one reader alone. We propose an ontology-enhanced probabilistic reasoning system for labeling gross anatomical structures i.e., maximally connected anatomical parts of non-microscopic size (thus anatomical structures like cells and molecules are not gross-level anatomical parts) and narrow our area of focus to CT data in the chest region. In observing how human readers analyze CT scans to determine if a pathology exists or not, we discovered that they primarily apply (1) their knowledge of the appearances of the gross anatomical structures (such as intensity, shape, size etc.); (2) their knowledge of the locations of structures relative to each other and (3)flag any unusual structures at unexpected locations. Hence, we incorporated these concepts in the design of the anatomy labeler. Different models for representing the appearance of anatomical structures have been investigated extensively in the areas of computer vision and medical image analysis and some popular intensity-based representations include edge/gradient measurements, textures and histograms. Others include spectral features, geometric representations. The study of the location and the arrangement of anatomical structures within the human body (for interpretation by computers) has been much less investigated and we argue that a good machine-based human anatomy segmentation/labeling system will need to incorporate both concepts. The representation of spatial relations between body parts is the main component of an anatomical ontology. The quality of such an ontology is determined by how unambiguous the use of its relational terms are, and ambiguity is avoided by a clear understanding/definition of what exactly the relational terms denote. Foundational Model of Anatomy (FMA) is currently the most comprehensive ontology of human canonical anatomy consisting of approximately 75,000 classes and over 120,000 terms. It has over 2.1 million relationship instances from 168 relationship types and contains organs, their parts, as well as organ systems and body parts (body regions). FMA also includes definitions of macromolecules, cells and their parts, tissues etc. Although much research has gone into the development of the FMA, it is still not strongly suitable for supporting machine-based intelligence in its current state and we discuss this in more detail in the ensuing sections. The goal of this research work is to provide succinct, unambiguous definitions for the relational terms in an anatomical ontology, integrating the ontology into a probabilistic reasoning framework to better label the anatomical parts of a section of the human body. 2 Symbolic Modeling Principles The representation of spatial relations between body parts includes mereological relations such as parthood and overlap, topological relations such as connectedness and one-pieceness, as well as location relations such as located in and partially coincides. We use qualitative relations (rather than exact measurements) because of the many variations that often occur in the human anatomy from one individual to another. For example, according to the Atlas of Vascular Anatomy [20] there is a number of variations in the origin of aortic arch branches. There is a usual pattern that occurs in about 65% of the population, a less common pattern in about 27%, and three other varieties, the least popular occurring in only 1.2%. Hence, for an automated reasoning system, it is important to design a universal model where any variations observed will be flagged either as a normal abberation or a pathology. The qualitative spatial relations will be later exploited for probabilistic reasoning. In this section, we briefly review the qualitative spatial relations as relevant for this paper. For Details see [8]. Parthood, symbolized as P , is the relation that holds between two entities, x and y, whenever x is part of y. For example, the heart is part of the chest (or thorax). Other relations, such as overlap (having a common part) (DO) are defined in terms of parthood. For example, my hand is part of my body and my hand and my body overlap. Location relations are defined terms of a region function, r, that maps each spatial entity to the spatial region at which is exactly located at the given moment. The function r is treated as a time-independent primitive hence the notion of movement is absent in our ontology. x is located in y if x’s spatial region is part of y’s spatial region (DLocIn); x and y partially coincide if x’s spatial region and y’s spatial region overlap (DPCoin). For example, the right lung is located in (but not part of) the thoracic cavity. The esophagus partially coincides with the mediastinal space. The relation LocIn does not hold since the esophagus’ region is not part of the region of the mediastinal space, i.e. part of the esophagus lies outside of the mediastinal space. Also, a catheter or stent inside a patients chest is located-in (but not part of) the patients chest. The “located-in” relation does not directly correspond to the English expression “is-in”. Two entities x and y are connected, Cxy, if and only if x and y overlap or x and y are in direct external contact. We define: x and y are externally connected if and only if x and y are connected and x and y do not partially coincide (DEC); x and y are separated if and only if x and y are not connected (DSP ).