Available 01.10.2015 – 30.09.2018
Sponsored by KWF, Storz GmbH
Cooperation(s) Lakeside Labs
Contact Assoc.-Prof. DI Dr. Klaus Schöffmann
E-Mail Write an E-Mail
Phone +43 (0)463 2700 3620
Fax +43 (0)463 2700 3620 993620
Employees  
Dipl.-Ing. Sabrina Kletz

Dipl.-Ing. Sabrina Kletz

Project Assistant
Room
E.2.48a
Projects
KISMET, SQUASH
Dipl.-Ing. Stefan Petscharnig

Dipl.-Ing. Stefan Petscharnig

Project Assistant
Room
E.2.50
Short CV
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.

Projects
KISMET, AdvUHD-DASH
Dipl.-Ing. Dr. Manfred Jürgen Primus

Dipl.-Ing. Dr. Manfred Jürgen Primus

Project Assistant
Room
E.1.41
Fax
+43 (0)463 2700 993635

 

Projects
ENDOVIP2, Code-MM, KISMET

Description

These days many surgeries are performed with a minimally-invasive approach, also known as medical endoscopy or keyhole-surgery. There are several special areas of medical endoscopy, the most frequent ones are arthroscopy (operations performed on joints), colonoscopy (procedures in the colon), and laparoscopy (operations in the abdomen). The endoscope is equipped with a light source, some fiber optics, and a high-resolution video camera, whose images are transmitted to a large display in the operation room. The images on this display are used by the operating endoscopists to control the endoscope and supervise actions performed with the operation instruments. Surgeons nowadays also record the real-time images of the endoscope as digital videos, stored as video segments for the most important parts of the surgery, or as a full video for the whole surgery, due to several reasons. In the EndoVIP2 project we focus on processing of these videos, performing video content analysis to detect relevant content, developing efficient storage methods for these videos, and providing efficient content search for that particular video data.