CLAIRE—Parallelized Diffeomorphic Image Registration for Large-Scale Biomedical Imaging Applications
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 16, 2022
- Source ID
- 10.3390/jimaging8090251
Entities
People
- Andreas Mang
- George Biros
- Jae-youn Kim
- Malte Brunn
- Miriam Schulte
- Naveen Himthani
Organizations
- Air Force Office of Scientific Research
- German Research Foundation
- National Institutes of Health
- National Science Foundation
- Nvidia
- United States Department of Energy