Ontology-Guided Markerless Navigation and Situational Awareness for Endoscopic Surgery.

Optical navigation systems help surgeons find their way through the complex anatomy of a patient. However, such systems are accident-sensitive, time-consuming and difficult to use because of their complicated technical requirements such as the setting of optical markers and their intraoperative registration. The BIOPASS project, therefore, provides an innovative localisation system for markerless navigation in endoscopic surgery to support medical decision making. This system comprises several machine learning classifiers to recognise anatomical structures visible in the endoscopic images. To verify the data provided by these classifiers and to alert medical staff about surgical risk situations, we developed a new ontology-based software called OntoSun. Our software improves the precision and the sustainable traceability of the classifiers' results and also provides warning messages that increase situational awareness during surgical interventions.

Processual Reasoning over Sequences of Situations in Endoscopic Surgery.

Minimally invasive surgery is a highly complex and technically demanding alternative to open surgery. Surgical procedures based on this method are characterized by small incisions and allow for a fast recovery of the patient. Such techniques are challenging for surgeons since they do not have a direct view of the surgical area. Systems that provide surgical navigation are well established in clinical practice but depend on external markers allowing a mapping between a surgeon's tools and a patient's medical images. As of today, these systems are prone to inaccuracies, the reasons of which lie in their extensive technical requirements. The BIOPASS project aims to develop an alternative that works without external markers and indirect computation of locations. An ontology has been used to provide an adequate vocabulary describing situations and their temporal relationship. This ontology is expected to relate real time multimodal sensor data and static surgical process models in order to infer movement directions, subsequent actions and hidden anatomical structures that inhere risk for surgical interventions. However, the Web Ontology Language is not capable of modelling temporal conditions, which are necessary to provide such exhaustive situational descriptions as expected by a surgeon. This paper concerns an ontology design pattern developed to overcome this issue by the integration of dynamic ontological classes that are assigned according to the temporal relations between situations.