Mathias Lux, Michael Riegler, Pål Halvorsen, Konstantin Pogorelov, Nektarios Anagnostopoulos
2016 Proceedings of the 7th International Conference on Multimedia Systems - MMSys '16  
Advanced and automated medical systems have been in the research focus for a long time. Together with the rapid development of sensing devices, the modern information analysis methods allow the new wave of computer-assisted systems to improve health care, quality of life, and patient survival rate. Together with the traditional computer vision and medical imaging, core competencies of the multimedia community such as integration and analysis of data from several sources, real-time processing
more » ... the assessment of usefulness for end-users play an essential role for the successful improvement of health care systems addressing challenges and open problems in the field of medicine. Our work explores different fields in multimedia research, starting from collection and annotation of multimedia data through automatic analysis of content and efficient processing of workloads to visualization and results representation. We have researched and developed a holistic medical multimedia system addressing a use case with an important medical and societal impact. We target lesions and findings detection and localization in the gastrointestinal (GI) tract of the human body in order to be able to support medical experts in their daily routine work. The early and precise detection of abnormalities in the GI tract greatly increases the chance of successful treatment if the initial observation of disease indicators occurs before the patient notices any symptoms, it is a non-trivial task that can be, however, efficiently automated. We investigated the GI tract visual analysis from a multimedia research point of view via several steps of research and development. First, we looked into the problem of medical data acquisition. We collected, annotated, and published several datasets and data annotation tools as open source. Then, we designed and developed a set of lesion and findings detection and localization approaches based on hand-crafted methods as well as on global-, local-and deepfeature-based methods, which serves as the algorithmic basis of our system. Next, we created a holistic medical multimedia system called DeepEIR. We researched and developed different subsystems for our DeepEIR system, namely (i) the data exploration and annotation subsystem, which makes it possible to collect and annotate data and transfer knowledge from medical experts into our system; (ii) the detection and localization subsystem, which perform medical data analysis in order to detect and localize lesions and findings; and (iii) the visualization and results representation subsystem that provides the information to medical personnel. Furthermore, the focus of the DeepEIR system lies on the accurate and time-efficient processing of multimedia data. We investigated, therefore, parallel and distributed processing, GPU-based acceleration and different classification and segmentation approaches that are evaluated and compared with state-of-the-art methods, algorithms, and systems. We demonstrated that the DeepEIR system could outperform state-of-the-art approaches in both processing speed and detection accuracy reaching processing speeds above 300 frames per second, a frame-wise detection accuracy above 95% and pixel-wise localization accuracy above 90%. With our results good enough for the clinical trials and successful demonstration of full-scale prototypes of DeepEIR system, we were able to attract several hospitals for tight collaborations, and the DeepEIR system is being prepared for a broad testing and using under clinical conditions within our collaborating hospitals. Acknowledgements First, I would like to thank my three official PhD supervisors: Pål Halvorsen, Carsten Griwodz and Michael Riegler. I would like to especially thank: Pål for his supervision, useful advice and support. Carsten for his critical yet guiding discussions and feedback. Michael for being a research partner during our well-established work together. I would like to especially thank my current research supervisor Johannes Langguth for the provided possibility to finish my PhD thesis writing while working on our new research project. I would also like to give many thanks to all my current and former colleagues in Simula Research Laboratory. We were working, talking and having fun together. Thank you, my dear
doi:10.1145/2910017.2910630 dblp:conf/mmsys/LuxRHPA16 fatcat:jfweprlxfzb2lommgkw6nqqwi4