Stephen R. Aylward

TL;DR: A deformable image registration algorithm that uses anisotropic smoothing for regularization to find correspondences between images of sliding organs is proposed, showing that motion discontinuities caused by sliding can be effectively recovered, unlike conventional regularizations that enforce globally smooth motion.

TL;DR: What are gaps in the way medical image computing is pursued today; how a well-executed research platform can enable discovery, innovation and reproducible science ("Open Science"); and how the quest to build such a software platform has evolved into a productive and rewarding social engineering exercise in building an open-access community with a shared vision are discussed.

TL;DR: A novel model-to-image registration technique which aligns a 3-dimensional model of vasculature with two semiorthogonal fluoroscopic projections is developed, which shows speed, accuracy and consistency given clinical data.

TL;DR: The goals of the workshop were to identify gaps in the engineering infrastructure available to IGT researchers, develop the role of research funding agencies and the recently established US-based National Center for Image Guided Therapy (NCIGT), and ultimately to facilitate the transfer of technology among research centers that are sponsored by the National Institutes of Health (NIH).

TL;DR: This paper reports on several experiments whose purpose was to rank the relative significance of these error sources and thereby find neural network design principles for limiting the magnitude and variance of network errors.