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Diffeomorphic registration faces challenges for high dimensional images, especially in terms of memory limits. Existing approaches either downsample/crop original images or approximate underlying transformations to reduce the model size. To mitigate this, we propose a Dividing and Down-sampling mixed Registration network (DDR-Net), a general architecture that preserves most of the image information at multiple scales while reducing memory cost. DDR-Net leverages the global context via downsampling the input and utilizes local details by dividing the input images to subvolumes. Such design fuses global and local information and obtains both coarse- and fine-level alignments in the final deformation fields. We apply DDR-Net to the OASIS dataset. The proposed simple yet effective architecture is a general method and could be extended to other registration architectures for better performance with limited computing resources.
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CLAIRE—Parallelized Diffeomorphic Image Registration for Large-Scale Biomedical Imaging Applications
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.
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- PAR ID:
- 10433170
- Date Published:
- Journal Name:
- Journal of Imaging
- Volume:
- 8
- Issue:
- 9
- ISSN:
- 2313-433X
- Page Range / eLocation ID:
- 251
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/jimaging8090251
