Fast Predictive Simple Geodesic Regression

TitleFast Predictive Simple Geodesic Regression
Publication TypeConference Paper
Year of Publication2017
AuthorsDing Z., Fleishman G., Yang X, Thompson P., Kwitt R, Niethammer M.,
Conference NameMICCAI Workshop on Deep Learning in Medical Image Analysis (DLMIA)

Analyzing large-scale imaging studies with thousands of images is computationally expensive. To assess localized morphological differences, deformable image registration is a key tool. However, as registrations are costly to compute, large-scale studies frequently require large compute clusters. This paper explores a fast predictive approximation to image registration. In particular, it uses these fast registrations to approximate a simplified geodesic regression model to capture longitudinal brain changes. The resulting approach is orders of magnitude faster than the optimization-based regression approach and hence facilitates large-scale analysis on a single graphics processing unit. We show results on 2D and 3D brain magnetic resonance images from OASIS and ADNI.