Title: Just Enough Interaction Paradigm and Graph Algorithmic Techniques for Biomedical Image Segmentation
Speaker: Milan Sonka, Associate Dean for Research and Graduate Programs, University of Iowa
Abstract: Accurate and reliable image segmentation is of paramount importance in medical image analysis. With a widespread use of 3D/4D imaging modalities like MR, MDCT, ultrasound, or OCT in routine clinical practice, physicians are faced with ever-increasing amounts of image data to analyze and quantitative outcomes of such analyses are increasingly important. Yet, daily interpretation of clinical images is still typically performed visually and qualitatively, with quantitative analysis being an exception rather than the norm. Since performing organ/object segmentations in 3D or 4D is infeasible for a human observer in clinical setting due to the time constraints, quantitative and highly automated analysis methods must be developed. For clinical acceptance, the method must be robust in clinical-quality images and must offer close-to 100% success rate – possibly using minimal expert-user guidance following the Just Enough Interaction (JEI) paradigm.
Our method for simultaneous segmentation of multiple interacting surfaces belonging to multiple interacting objects will be presented. The reported method is part of the family of graph-based image segmentation methods dubbed LOGISMOS for Layered Optimal Graph Image Segmentation of multiple Objects and Surfaces. This family of methods guarantees solution optimality with directly applicability to n-D problems. To solve the issue of close-to 100% performance in clinical data, the JEI paradigm is inherently tied to the LOGISMOS approach and allows highly efficient minimal (just-enough) user interaction to refine the automated segmentation. Clinically acceptable results are obtained in each and every analyzed scan with no or only small increase in human analyst effort. The performance of the minimally-guided JEI method will be demonstrated on pulmonary CT and coronary IVUS image data.
Speaker Bio: Milan Sonka received his Ph.D. degree in 1983 from the Czech Technical University in Prague, Czech Republic. He is Associate Dean for Graduate Programs and Research of the College of Engineering at the University of Iowa, Professor of Electrical & Computer Engineering, Professor of Ophthalmology & Visual Sciences, and Radiation Oncology, Director of the Iowa Institute for Biomedical Imaging, IEEE Fellow, and AIMBE Fellow. His research interests include medical imaging and knowledge-based image analysis with emphasis on cardiovascular, pulmonary, orthopedic, cancer, and ophthalmic image analysis. He is the first author of 4 editions of Image Processing, Analysis and Machine Vision book (1993, 1998, 2008, 2014) and co-authored or co-edited 20 books/proceedings. He has published more than 140 journal papers and over 340 other publications. He is Editor in Chief of the IEEE Transactions on Medical Imaging and member of the Editorial Board of the Medical Image Analysis journal. To bring results of his research work to clinical practice, he has co-founded two medical imaging companies — Medical Imaging Applications LLC, and VIDA Diagnostics Inc.