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Abstract An unsupervised machine learning method is introduced to align medical images in the context of the large deformation elasticity coupled with growth and remodeling biophysics. The technique, which stems from the principle of minimum potential energy in solid mechanics, consists of two steps: Firstly, in the predictor step, the geometric registration is achieved by minimizing a loss function composed of a dissimilarity measure and a regularizing term. Secondly, the physics of the problem, including the equilibrium equations along with growth mechanics, are enforced in a corrector step by minimizing the potential energy corresponding to a Dirichlet problem, where the predictor solution defines the boundary condition and is maintained by distance functions. The features of the new solution procedure, as well as the nature of the registration problem, are highlighted by considering several examples. In particular, registration problems containing large non-uniform deformations caused by extension, shearing, and bending of multiply-connected regions are used as benchmarks. In addition, we analyzed a benchmark biological example (registration for brain data) to showcase that the new deep learning method competes with available methods in the literature. We then applied the method to various datasets. First, we analyze the regrowth of the zebrafish embryonic fin from confocal imaging data. Next, we evaluate the quality of the solution procedure for two examples related to the brain. For one, we apply the new method for 3D image registration of longitudinal magnetic resonance images of the brain to assess cerebral atrophy, where a first-order ODE describes the volume loss mechanism. For the other, we explore cortical expansion during early fetal brain development by coupling the elastic deformation with morphogenetic growth dynamics. The method and examples show the ability of our framework to attain high-quality registration and, concurrently, solve large deformation elasticity balance equations and growth and remodeling dynamics.
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Eurographics Workshop on Visual Computing for Biology and Medicine
We introduce a novel multi-modal 3D image registration framework based on 3D user-guided deformation of both volume's shape and intensity values. Being able to apply deformations in 3D gives access to a wide new range of interactions allowing for the registration of images from any acquisition method and of any organ, complete or partial. Our framework uses a state of the art 3D volume rendering method for real-time feedback on the registration accuracy as well as the image deformation. We propose a novel methodological variation to accurately display 3D segmented voxel grids, which is a requirement in a registration context for visualizing a segmented atlas. Our pipeline is implemented in an open-source software (available via GitHub) and was directly used by biologists for registration of mouse brain model autofluorescence acquisition on the Allen Brain Atlas. The latter mapping allows them to retrieve regions of interest properly identified on the segmented atlas in acquired brain datasets and therefore extract only high-resolution images of those areas, avoiding the creation of images too large to be processed.
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- Award ID(s):
- 2136744
- PAR ID:
- 10474741
- Editor(s):
- G., Renata Raidou; Sommer, Björn; W., Torsten Kuhlen; Krone, Michael; Schultz, Thomas; Wu, Hsiang-Yun
- Publisher / Repository:
- The Eurographics Association
- Date Published:
- 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
- Conference Paper:
- https://doi.org/10.2312/vcbm.20221191
