CLAIRE-PARALLELIZED DIFFEOMORPHIC IMAGE REGISTRATION FOR LARGE-SCALE BIOMEDICAL IMAGING APPLICATIONS

作     者：Himthani, Naveen Brunn, Malte Kim, Jae-Youn Schulte, Miriam Mang, Andreas Biros, George 

作者机构：Oden Institute The University of Texas at Austin AustinTX United States Institute for Parallel and Distributed Systems University of Stuttgart DE Stuttgart Germany Department of Mathematics University of Houston HoustonTX United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Image registration 

摘      要：We study the performance of CLAIRE-a diffeomorphic multi-node, multi-GPU image registration algorithm and software-in large-scale biomedical imaging applications with billions of voxels. At such resolutions, most existing software packages for diffeomorphic image registration are prohibitively expensive. As a result, practitioners first significantly downsample the original images and then register them using existing tools. Our main contribution is an extensive analysis of the impact of downsampling on registration performance. We study this impact by comparing full resolution registrations obtained with CLAIRE to lower resolution registrations for synthetic and real-world imaging datasets. Our results suggest that registration at full resolution can yield a superior registration quality-but not always. For example, downsampling a synthetic image from 10243 to 2563 decreases the Dice coefficient from 92% to 79%. However, the differences are less pronounced for noisy or low contrast high resolution images. CLAIRE allows us not only to register images of clinically relevant size in a few seconds but also to register images at unprecedented resolution in reasonable time. The highest resolution considered are CLARITY images of size 2816 × 3016 × 1162. To the best of our knowledge, this is the first study on image registration quality at such resolutions. Copyright © 2022, The Authors. All rights reserved.