Anatomical Data Augmentation via Fluid-based Image Registration
- URL: http://arxiv.org/abs/2007.02447v1
- Date: Sun, 5 Jul 2020 21:06:00 GMT
- Title: Anatomical Data Augmentation via Fluid-based Image Registration
- Authors: Zhengyang Shen, Zhenlin Xu, Sahin Olut, Marc Niethammer
- Abstract summary: We introduce a fluid-based image augmentation method for medical image analysis.
In contrast to existing methods, our framework generates meaningful images via a geodesic subspace.
- Score: 23.280420626023755
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce a fluid-based image augmentation method for medical image
analysis. In contrast to existing methods, our framework generates anatomically
meaningful images via interpolation from the geodesic subspace underlying given
samples. Our approach consists of three steps: 1) given a source image and a
set of target images, we construct a geodesic subspace using the Large
Deformation Diffeomorphic Metric Mapping (LDDMM) model; 2) we sample
transformations from the resulting geodesic subspace; 3) we obtain deformed
images and segmentations via interpolation. Experiments on brain (LPBA) and
knee (OAI) data illustrate the performance of our approach on two tasks: 1)
data augmentation during training and testing for image segmentation; 2)
one-shot learning for single atlas image segmentation. We demonstrate that our
approach generates anatomically meaningful data and improves performance on
these tasks over competing approaches. Code is available at
https://github.com/uncbiag/easyreg.
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