|Available||01.10.2015 – 30.09.2018|
|Sponsored by||KWF, Storz GmbH|
|Contact||Assoc.-Prof. DI Dr. Klaus Schöffmann|
|Phone||+43 (0)463 2700 3620|
|Fax||+43 (0)463 2700 3620 993620|
Dipl.-Ing. Sabrina Kletz
Dipl.-Ing. Stefan Petscharnig
Stefan Petscharnig started his studies of computer science in 2010 at AAU Klagenfurt. He received his bachelor’s degree in 2014 and his master’s degree in 2015 from AAU Klagenfurt. In his master’s thesis he investigated the impact of asynchronism on the Quality of Experience in social TV like scenarios using methods from Crowdsourcing, Human Computation, and Gamification. He was occupied as student research assisstant and after graduation as project assistant for the AdvUHD-DASH project in 2015. Since 2016, he works in the KISMET research project with a focus on the analysis of endoscopic video data using machine learning.
In the field of endoscopic surgery, modern information technology brought revolutionary improvements in the last decade. Physicians are enabled to perform minimally invasive surgeries with the help of high resolution cameras, to follow the progress of the surgery on large, high definition (HD) video screens and to record and store the videos of the interventions. These techniques are on the highest level of state-of- the-art technology. On the other hand, the further destiny of the recorded endoscopic videos falls into a technological gap. Tools for further exploration, such as search, interaction and sharing are poor or non-existing at all. The main goal of the KISMET project is to fill this gap, at least to a certain extent, with novel and competitive tools.
We concentrate on a specific area, on endoscopy in gynecology, in particular in endometriosis, relying on the world-wide admitted practices of the LKH Villach in this area. We apply basic and applied research methods to investigate the current situation in the hospital LKH Villach, identify problems in the workflows related to multimedia content, develop possible improvements based on a self-organizing cooperative system and evaluate this system in day-to-day usage scenarios. The three main research questions of the project are (i) how to gain expert knowledge from physicians in a convenient way, without imposing additional workload on them, (ii) how to adapt state-of-the-art multimedia content indexing methods to the specific characteristics of medical multimedia data based on expert knowledge and (iii) how to support sharing of data and metadata among the experts (enabling an enhanced communication with patients as well).