A Large Deformation Diffeomorphic Approach to Registration of CLARITY Images via Mutual Information

@inproceedings{Kutten2016ALD,
  title={A Large Deformation Diffeomorphic Approach to Registration of CLARITY Images via Mutual Information},
  author={Kwame S. Kutten and Nicolas Charon and Michael I. Miller and J. Tilak Ratnanather and Jordan K. Matelsky and Alex Baden and Kunal Lillaney and Karl Deisseroth and Li Ye and Joshua T. Vogelstein},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  year={2016}
}
CLARITY is a method for converting biological tissues into translucent and porous hydrogel-tissue hybrids. This facilitates interrogation with light sheet microscopy and penetration of molecular probes while avoiding physical slicing. In this work, we develop a pipeline for registering CLARIfied mouse brains to an annotated brain atlas. Due to the novelty of this microscopy technique it is impractical to use absolute intensity values to align these images to existing standard atlases. Thus we… 
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16 Citations
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16 Citations

CLAIRE—Parallelized Diffeomorphic Image Registration for Large-Scale Biomedical Imaging Applications

An extensive analysis of the impact of downsampling on registration performance in large-scale biomedical imaging applications with billions of voxels suggests that registration at full resolution can yield a superior registration quality—but not always.

DiffeoRaptor: diffeomorphic inter-modal image registration using RaPTOR

This work proposed a novel inter-modal/contrast registration algorithm that leverages Robust PaTch-based cOrrelation Ratio metric to allow inter- modal/Contrast image alignment and bandlimited geodesic shooting demonstrated in Fourier-Approximated Lie Algebras (FLASH) algorithm for fast diffeomorphic registration.

Fast diffeomorphic image registration via GPU-based parallel computing: an investigation of the matching cost function

Comparing the three matching cost functions, it is observed that LDDMM-CC worked the best in terms of registration accuracy, obtaining Dice overlaps larger than 0.853 for a majority of structures of interest.

Expanding the Computational Anatomy Gateway from clinical imaging to basic neuroscience research

A new approach to multimodality registration is developed that can replace ad hoc image similarity metrics (such as mutual information or normalized cross correlation) with a log likelihood under a noise model, enabling the community to benefit from atlas based computational image analysis techniques.

Joint Atlas-Mapping of Multiple Histological Series combined with Multimodal MRI of Whole Marmoset Brains

This work presents a fully automatic pipeline for same-subject-MRI guided reconstruction of image volumes from a series of histological sections of different modalities, followed by diffeomorphic mapping to a reference atlas.

CLAIRE: Constrained Large Deformation Diffeomorphic Image Registration on Parallel Computing Architectures

CLAIRE implements different preconditioners for the reduced space Hessian (Brunn et al., 2020; Mang et al, 2019) to optimize computational throughput and enable fast convergence.

Developing a brain atlas through deep learning

A fully automated deep neural network-based method is introduced for automated registration through segmenting brain regions of interest with minimal human supervision to accelerate brain-wide exploration of region-specific changes in brain development.

Developing a brain atlas through deep learning

A deep learning-based method is developed for automated registration through segmenting brain regions of interest with minimal human supervision that can accelerate brain-wide exploration of region-specific changes in brain development and offer an alternative to existing complex brain registration techniques.

An optimized tissue clearing protocol for rat brain labeling, imaging, and high throughput analysis

AdipoClear+, an optimized immunolabeling and clearing methodology for application to adult rat brain hemispheres is introduced, and its application is validated through the testing of common antibodies and electrode tract visualization.

PDE-constrained optimization in medical image analysis

The ultimate goal of the work is the design of inversion methods that integrate complementary data, and rigorously follow mathematical and physical principles, in an attempt to support clinical decision making, which requires reliable, high-fidelity algorithms with a short time-to-solution.

References

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