Topological Data Analysis and its Applications for Medical Data

Dr. Chao Chen is an assistant professor at Stony Brook University. His research interest spans topological data analysis (TDA), machine learning and biomedical image analysis. He develops principled learning methods inspired by the theory from TDA, such as persistent homology and discrete Morse theory. These methods address problems in biomedical image analysis, robust machine learning, and graph neural networks from a unique topological view. His research results have been published in major machine learning, computer vision, and medical image analysis conferences. He is serving as an area chair for MICCAI, AAAI, CVPR and NeurIPS.

Title of the keynote talk : Biomedical Image Analysis with Topological Information

Abstract : Thanks to decades of technology development, we are now able to visualize in high quality complex biomedical structures such as neurons, vessels, trabeculae and breast tissues. We need innovative approaches to fully exploit these structures, which encode important information about underlying biological mechanisms. In this talk, we explain how topological information can be seamlessly incorporated into different parts of a learning pipeline, based on the theory of topological data analysis. This leads to a series of novel methods for better segmentation, generation, and analysis of these topology-rich biomedical structures.

Moo K. Chung, Ph.D. is an Associate Professor in the Department of Biostatistics and Medical Informatics at the University of Wisconsin-Madison (http://www.stat.wisc.edu/~mchung). He is also affiliated with the Waisman Laboratory for Brain Imaging and Behavior and Department of Statistics. Chung’s research focuses on topological data analysis, computational neuroimaging and brain network analysis. His research concentrates on the methodological development required for quantifying and contrasting functional, anatomical shape and network variations in both normal and clinical populations using various mathematical, statistical and computational techniques. He has published three books on neuroimage computation including Brain Network Analysis that was recently published through Cambridge University Press in 2019.

Title of the keynote talk : Lattice Paths for Persistent Diagrams with Application to COVID-19 Virus Spike Proteins

Abstract : Persistent homology has undergone significant development in recent years. However, one outstanding challenge is to build a coherent statistical inference procedure on persistent diagrams. In this talk, we first present a new lattice path representation for persistent diagrams. We then develop a new exact statistical inference procedure for lattice paths via combinatorial enumerations. The lattice path method is applied to the topological characterization of the protein structures of the COVID-19 virus. We demonstrate that there are topological changes during the conformational change of spike proteins.

Punam Kumar Saha is a tenured professor of Electrical and Computer Engineering and Radiology at the University of Iowa since 2013. His research interests include image processing, deep learning, artificial intelligence, and quantitative biomedical imaging. He has published over 115 papers in international journals and over 300 papers/abstracts in international conferences, holds numerous patents related to medical imaging applications, has served as an associate editor of Pattern Recognition and Computerized Medical Imaging and Graphics journals and in many international conferences at various levels. Currently, he is an Associate Editor of the IEEE Transactions on Biomedical Engineering and the Pattern Recognition Letters journals. He won the Young Scientist award from the Indian Science Congress Association in 1996 and the CoE Faculty Excellence Award for Research at the University of Iowa in 2020. He has received several grant awards from the National Institute of Health, USA. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), American Institute for Medical and Biological Engineering (AIMBE), and International Association for Pattern Recognition (IAPR). He has served as a member of several IEEE and AIMBE Fellow committees.

Title of the keynote : Osteoporosis, Bone Microarchitecture, and Imaging – Recent Developments and Translational Studies

Abstract : Osteoporosis is a common age-related disease characterized by reduced bone density and increased fracture-risk. Approximately, 40 percent of women and 13 percent of men suffer one or more osteoporotic fractures in their lifetime, which reduce quality of life and, often, lead to immobility and mortality. Osteoporotic hip fractures reduce life expectancy by 20 percent and add an annual healthcare cost of nearly 19 billion dollars in the United States only. Early and accurate diagnosis of osteoporosis and assessment of fracture-risk is fundamental to handle the disease, and bone imaging plays an important role to accomplish this goal. Dual-energy X-ray absorptiometry (DXA) measured bone mineral density (BMD) is clinically used to characterize osteoporosis. It is known that BMD explains 60-70% of the variability in bone strength and fracture-risk, and the remaining variability comes from collective effects of other factors such as cortical and trabecular bone distribution, and their micro-structural basis. Accurate and robust measurement of effective cortical and trabecular bone microstructural features, associated with bone strength and fracture-risk, is of paramount clinical significance. State-of-the-art in vivo imaging modalities for bone microstructural assessment include magnetic resonance imaging (MRI), high-resolution peripheral quantitative computed tomography (HR-pQCT), flat-panel cone beam CT (CBCT), and whole-body multi-row detector CT (MDCT). Different research groups have applied various methods for characterization of bone microstructure related to cortical porosity and thickness, trabecular volume, network area, spacing, number, star volume measure, structure model index, connectivity number etc. Our research group has developed unique methods for in vivo clinical CT-based assessment of cortical porosity and trabecular plate-rod and longitudinal-transverse micro-architecture. This talk presents the principles and basis of these methods, experimental results evaluating their fidelity, generalizability, and impact on translational and clinical research studies.