GRK 2543:  Intraoperative multi-sensor tissue identification in oncology

• Subject Area: Systems Engineering ; Medicine
• Term: since 2020
• Website: Homepage
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 409474577

New surgical methods aim to minimize invasiveness, morbidity and duration of the treatment while maximizing treatment effectiveness. During these surgical interventions, a reliable identification of target structures and surrounding tissue is of utmost importance for achieving this objective, particularly in the field of oncology. Current innovations significantly improve pre- and postoperative diagnostics for distinguishing between benign and malignant tissue structures. Today, refined imaging systems can guide presurgical decision-making more accurately, while enhanced histopathological examination methods allow for a reliable classification of the dissected tissue after surgery. The graduate college, however, focuses on intraoperative tissue identification. The fusion of novel multimodal sensor systems by means of machine learning offers a high potential for new procedures to discriminate between tissues that goes beyond the information content of the separate sensor data. The presented methods supplement the gold standard of intraoperative tissue identification, i.e. frozen section diagnostics. As a result, we provide additional information to support surgeons in their decision making between resection and tissue preservation.The graduate college envisions the development of new multimodal approaches for the intraoperative identification of tissue structures on the basis of gynecological and urological application scenarios. Its research approach complements the established histological and imaging techniques and merges information from multiple sensors to ensure reliable tissue identification. In addition to optical tissue properties, which are assessed using IR or Raman spectroscopy, multimodal sensor systems will also capture electrical and mechanical parameters to increase the reliability of tissue identification. The corresponding evaluation methods as well as the tissue differentiation capacity of active surgical instruments, e.g. from waterjet surgery, are analyzed. Our research in the fields of model-based analysis, multimodal sensor fusion and machine learning will combine information from all available sources and eventually lead to a comprehensive evaluation of the tissue. The challenges of the highly heterogeneous research environment of medical technology are met with an interdisciplinary mentoring and qualification program. The members of the graduate college are supervised by a team with complementary expertise and are assigned to all involved scientific fields. This integrated training uniquely qualifies the fellows for future research activities in the field of medical engineering. The proposed concept constitutes, after the establishment of the ‚Interuniversitary Center for Medical Technologies Stuttgart-Tübingen (IZST)’ and joint degree courses in medical engineering, the next logical step for a close collaboration of both universities in the field of medical technology.

• DFG Programme: Research Training Groups
• Applicant Institution: Universität Stuttgart
• Co-Applicant Institution: Eberhard Karls Universität Tübingen
• Spokesperson: Professor Dr.-Ing. Oliver Sawodny
• Participating Researchers: Privatdozent Dr. Bastian Amend; Professorin Dr. Sara Brucker; Professor Dr. Falko Fend; Professor Dr. Alois Herkommer; Professor Dr.-Ing. Hendrik Lensch; Professorin Dr. Katja Schenke-Layland; Professor Dr. Tilman Schäffer; Professor Dr. Arnulf Stenzl; Professorin Dr.-Ing. Cristina Tarín Sauer