Optical navigation systems are the means of choice to overcome spatial association problems of the endoscopic imaging in minimally invasive surgery. Using optical markers, the patient's real position, his medical imaging data and surgical tool locations are mapped into the same workspace. Such visual-based assistance systems however, suffer from their technical requirements. The BIOPASS project aims to develop a navigation system based on a novel marker less localization method that uses only the current surgical situation and the procedure's history to identify the present anatomy. The ontology, presented in this paper, plays an integral part in this system as it translates the situational information of a surgical procedure into an internal machine-readable representation. This representation combines multimodal sensor data, e.g. endoscopic images, endoscope movement or surgical work steps, to allow a classification of the apparent situation and provide navigation support based on identified anatomical landmarks and work steps. Furthermore, it is a foundation of situational awareness based on spatiotemporal reasoning.
Optical surgical navigation systems significantly reduce the cut-seam-time leading to
improved post-operative results [
The BIOPASS Situation Ontology (BISON) functions as data model that unifies the
apparent endoscope location and the current work step [
1.1. General Formal Ontology
The modelling of situational knowledge is carried out within the framework of the
General Formal Ontology (GFO) being developed at the University of Leipzig [
Concepts are a special type of categories that have a close relation to language; predicate forms, being expressions of a natural or formal language, describe them. , and are their categorization of individuals. A continuant persists through time and has a lifetime, whereas a Process happens in time and is said to have a temporal extension. A continuant’s lifetime is a process, thus, we consider continuant as well as processes as being processual individuals. At any time point of this lifetime, a continuant a uniquely determined entity, called presential, which is wholly present at this time point.
There is a basic classification of processes with respect to their structural
constitution in GFO. At two coinciding process boundaries, which are described in [
A is a temporally extended part of the world, which can be understood as a
whole. A can be understood as the _ a situoid to a timepoint. A
_ _ a situoid (resp. situation) is an object involved in it. These notions
rely partially on the situation theory in [
An ontology, which is adequate for the given use case, must find a way to describe
spatiotemporally changing entities. To achieve such a representation, we utilized layers
as shown in figure 2. Since BISON has been implemented in OWL [
Knowledge shared between all components of the BIOPASS system is encoded in the conceptual layer. The elements of the class _ reflect process models that specify the workflow of specific surgical interventions. A constituent of a surgical concept is an element of the class _ and ’s instances can occur during an instance of . The object property _ _ _ relates an anatomical concept to an anatomical concept if it is expected that all instances of are following some instance of . Thus, this relation yields representations of surgical process models as graphs. The descriptions of specific FESS interventions were analysed to generate such conceptual graph, which, combined with BISON, is a task ontology.
The processual layer holds all entities that are processual as defined earlier. The elements of the class _ are situoids and specific to a patient on which they are executed. If a surgical intervention is instance of a surgical concept ; and ′s parts are generated automatically according to ′s conceptual graph when a surgeon has chosen to execute this kind of surgical intervention. Thus, surgical concepts define templates for surgical interventions in general. The generated parts of a surgical intervention are elements of the class _ being a subclass of and instances of anatomical concepts. The conceptual order of the corresponding anatomical concepts is reflected via the object property _ .
The presentic layer represents the content of endoscopical images and sensor data provided by the system. Each element of the class _ is a partially reconstructed physical situation based on this data and it _ a possibly existing predecessor. A new situation is created if: (a) landmark changes have been detected by the image processors, (b) the sensors detected a movement of the endoscope. In case of (a), elements of the class _ are generated and asserted to be constituent parts of the surgical situation as wells as exhibited by an anatomical object that is part of the executed surgical intervention. In case of (b), the direction in which the endoscope has been moved is asserted, i.e. the situation _ a discrete process according to GFO, i.e. a _ resp. _ .
During a surgical intervention, it is necessary to rule out incorrectly detected anatomical
structures. This function is implemented by the inference of movements presented in [
The BIOPASS system will provide a decision support option that suggests the next landmarks that have to be visited by the surgeon based on a classifier. However, it is nearly impossible that statistical predictions will have full precision. Hence, BISON infers all anatomical structures that can be visited in the following surgical situation to enhance the classifiers’ precision further. An anatomical object having the disposition to exhibit an anatomical structure that is a constituent part of the following surgical situation is a _ _ , which is a logical concept and defined in Eq. (1). Nearby_anatomical_object ≡ Not_occuring_anatomical_object and spatially_follows some Occuring_anatomical_object (1)
It is necessary to determine the role of anatomical structures that are visible in the most current endoscopical image. The two most important roles in FESS are: (a) landmark, which defines the need to be visited during a particular surgical intervention, and (b) risk structure, which defines an easily damageable anatomical structure. If the BIOPASS system can infer that an individual plays such processual role, it can display them accordingly and show warning messages. We introduced the object properties _ _ and _ _ and used them to solve this problem of role assignment.
Assume an anatomical concept that categorially plays role , which is a _ and categorial role of a surgical concept . For each ′ that is an instance of , there will be role ′, which is an instance of and role of ′, and an anatomical object ′, which is an instance of and plays ′. By this definition, BISON can support processual roles as part of the conceptual layer with corresponding subclasses of _ . Eventually, we could introduce the OWL class _ and _ _ as in Eq. (2) resp. analogous to Eq. (2). However, risk structures are mostly not visible in an endoscopical image, i.e., if they are behind an anatomical landmark. Thus, Eq. (3) is a secondary definition of this notion. Anatomical_landmark ≡ Anatomical_structure and exhibited_by some
(Occuring_anatomical_object and plays_role some Landmark_object) Anatomical_object and plays_role some Surgical_risk_object and spatially_follows some Occuring_anatomical_object ⊑ exhibits some Anatomical_risk_structure (2) (3)
In this paper, we introduced BISON, a situation ontology that implements a formalized description of minimally-invasive surgical procedures based on situational information extracted from endoscopic, procedural and sensory data. BISON utilizes ontological layers, which are implicitly provided by GFO. These layers specify the conceptualization of incorporated knowledge entities, their procedural characteristics, and, furthermore, their situational manifestation. BISON was implemented in a web application module to infer several process-related properties, e.g. anatomical landmark consecutiveness and plausibility as well as endoscope movement. Subsequent work steps will include the extension of situation concepts and their presentic manifestations as well as the abstraction of reasoning output into a comprehensible, user-friendly verbalization of the surgical situation. Furthermore, the elaboration of movements and direction is still in a raw state and yields fundamental ontological problems that will be investigated in future works.