About 2.8 millions of new luminal GI cancers (esophagus, stomach, colorectal) are detected yearly in the world, and the mortality is about 65%. In addition to these cancers, numerous other chronic diseases affect the human GI tract. The most common ones include gastroesophageal reflux disease, peptic ulcer disease, inflammatory bowel disease, celiac disease and chronic infections. All have a significant impact on the patients’ health-related quality of life. Consequently, gastroenterology is one of the most significant medical branches.
If we for example look at colorectal cancer (CRC) with one of the highest incidences and mortality of the diseases in the GI tract, early detection is essential for prognosis. Mini-invasive endoscopic and surgical treatment is most often curative in early stages (I- II) with a 5-year survival probability of about 90%, but in advanced stages (III-IV), radiation and/or chemotherapy is often required, and it has a 5-year survival of only 10-30%. In this case, colonoscopy is considered to be the gold standard for the examination of the colon for early detection of cancer and precancerous pathology. However, it is not the ideal screening test. On average 20% of polyps are missed or incompletely removed (i.e., the risk of getting CRC largely depend on the endoscopists ability to detect polyps). It is also a demanding procedure requiring a significant time investment from the medical professional, and the procedure is unpleasant and can cause great discomfort for the patient. This may lead to reduced participation rates and less efficient screening. As a result, ongoing colonoscopy screening programs have a low attendance rate.
Our overall idea is to develop a system for automatic analysis of (video) data from the entire GI tract. As a first approach and to show how complex the target is we developed a multimedia system that supports doctors in disease detection in the GI tract. The main requirements of such a system are (i) easy to use, (ii) easy to extend to different diseases, (iii) real time handling of multimedia content, (iv) being able to be used as a live system and (v) high classification performance with minimal false negative classification results. Therefore, the system consists of three main parts: The annotation sub-system, the detection and automatic analysis sub-system, and the visualization and computer aided diagnosis sub-system.
Using the ASU Mayo polyp dataset, our prototype EIR, based on content-based visual information retrieval, achieved a detection accuracy of above 90% at a speed of about 300 frames per second, i.e., real-time feedback is enabled.
“Multimedia and Medicine: Teammates for Better Disease Detection and Survival”, Michael Riegler, Mathias Lux, Carsten Griwodz, Concetto Spampinato, Thomas de Lange, Sigrun L. Eskeland, Konstantin Pogorelov, Wallapak Tavanapong, Peter T. Schmidt, Cathal Gurrin, Dag Johansen, Håvard Johansen, Pål Halvorsen, Proceedings of ACM Multimedia (ACM MM), Amsterdam, The Netherlands, October 2016, pp. 968-977 [pdf] [DOI: 10.1145/2964284.2976760] [slides]
“GPU-accelerated Real-time Gastrointestinal Diseases Detection”, Konstantin Pogorelov, Michael Riegler, Pål Halvorsen, Peter Thelin Schmidt, Carsten Griwodz, Dag Johansen, Sigrun Losada Eskeland, Thomas de Lange, Proceedings of the International Symposium on Computer-Based Medical Systems (CBMS), Dublin, Ireland/Belfast, Northern Ireland, June 2016 [pdf]
“EIR – Efficient Computer Aided Diagnosis Framework for Gastrointestinal Endoscopies”, Michael Riegler, Konstantin Pogorelov, Pål Halvorsen, Thomas de Lange, Carsten Griwodz, Peter Thelin Schmidt, Sigrun Losada Eskeland, Dag Johansen, Proceedings of the International Workshop on Content-based Multimedia Indexing (CBMI), Bucharest, Romania, June 2016 [pdf]
“Explorative Hyperbolic-Tree-Based Clustering Tool for Unsupervised Knowledge Discovery”, Michael Riegler, Konstantin Pogorelov, Mathias Lux, Pål Halvorsen, Carsten Griwodz, Sigrun Losada Eskeland, Thomas de Lange, Proceedings of the International Workshop on Content-based Multimedia Indexing (CBMI), Bucharest, Romania, June 2016 [pdf]
“Computer Aided Disease Detection System for Gastrointestinal Examinations”, Michael Riegler, Konstantin Pogorelov, Jonas Markussen, Mathias Lux, Håkon Kvale Stensland, Thomas de Lange, Carsten Griwodz, Pål Halvorsen, Dag Johansen, Peter Thelin Schmidt, Sigrun L. Eskeland, Proceedings of the ACM Multimedia Systems Conference (MMSys), Klagenfurt am Wörthersee, Austria, May 2016 [DOI:10.1145/2910017.2910629]
“Efficient Processing of Videos in a Multi Auditory Environment Using Device Lending of GPUs”, Konstantin Pogorelov, Michael Riegler, Jonas Markussen, Hålkon Kvale Stensland, Pål Halvorsen, Carsten Griwodz, Sigrun Losada Eskeland, Thomas de Lange, Proceedings of the ACM Multimedia Systems Conference (MMSys),
Klagenfurt am Wörthersee, Austria, May 2016 [DOI: 10.1145/2910017.2910636]
“Expert Driven Semi-Supervised Elucidation Tool for Medical Endoscopic Videos” (demo), Zeno Albisser, Michael Riegler, Pål Halvorsen, Jiang Zhou, Carsten Griwodz, Ilangko Balasingham, Cathal Gurrin, Proceedings of the ACM Multimedia Systems Conference (MMSys), Portland, OR, USA, March 2015, pp. 73-76, [pdf] [DOI: 10.1145/2713168.2713184]
“Event Understanding in Endoscopic Surgery Videos”, Mario Guggenberger, Michael Riegler, Mathias Lux, Pål Halvorsen, Proceedings of the ACM International Workshop on Human Centered Event Understanding from Multimedia (HuEvent), Orlando, FL, USA, November 2014 pp. 17-22 [DOI: 10.1145/2660505.2660509]