The rapid advances in technology, data acquisition, storage and computing power have transformed medicine and from a traditional discipline that empowered the clinician to a quantitative data science approach that relies on signal, images, and their processing, manipulation, interpretation, and use for various applications. Imaging has become a quintessential tool that enables the non-invasive or minimally invasive exploration of the anatomy and function either to detect and diagnose disease, plan treatments, or guide and deliver therapies. However, to enable all of the above, access to computational modeling and visualization tools is as critical as image acquisition. This minisymposium is an interdisciplinary venue at the intersection of imaging and computing and welcomes participation from computer science, engineering, mathematical modeling, imaging science and other related fields. Featured presentations will range from algorithms for image computing to applications in computer-aided diagnosis and minimally invasive therapy, as well as mixed reality visualization for simulation, teaching and training.

High-order Curvilinear Tetrahedral Meshes of the Cardiac Anatomy

Suzanne Shontz (University of Kansas)

Mesh Adaptation-aided Image Segmentation

Simona Perotto (MOX, Politecnico di Milano)

Coupling Brain-Tumor Biophysical Models and Diffeomorphic Image Registration

Klaudius Scheufele (University of Stuttgart)

Medical Imaging and Visualization: Enabling Computer-assisted Diagnosis and Therapy

Cristian Linte (Biomedical Engineering and Center for Imaging Science, Rochester Institute of Technology)

Segmentation of Biomedical Images - Algorithms and Applications

João Manuel R. S. Tavares (Faculty of Engineering of the University of Porto)

High-resolution MRI-based heart models for image-guided electrophysiology procedures

Mihaela Pop (Sunnybrook Research Institute / University of Toronto)

Augmented Reality for Cardiac Interventions

Terry M. Peters (Robarts Research Institute/Western University)

Organizers:

Cristian Linte (Biomedical Engineering and Center for Imaging Science, Rochester Institute of Technology)

Suzanne Shontz (University of Kansas)

Keywords: