| Autor: |
Nejatbakhsh A; Departments of Neuroscience and Statistics, Columbia University, New York, USA., Dey N; Computer Science and Artificial Intelligence Lab, MIT, Massachusetts, USA., Venkatachalam V; Department of Physics, Northeastern University, Boston, USA., Yemini E; Department of Neurobiology, University of Massachusetts Chan Medical School, Worcester, USA., Paninski L; Departments of Neuroscience and Statistics, Columbia University, New York, USA., Varol E; Department of Computer Science and Engineering, New York University, New York, USA. |
| Abstrakt: |
Atlases are crucial to imaging statistics as they enable the standardization of inter-subject and inter-population analyses. While existing atlas estimation methods based on fluid/elastic/diffusion registration yield high-quality results for the human brain, these deformation models do not extend to a variety of other challenging areas of neuroscience such as the anatomy of C. elegans worms and fruit flies. To this end, this work presents a general probabilistic deep network-based framework for atlas estimation and registration which can flexibly incorporate various deformation models and levels of keypoint supervision that can be applied to a wide class of model organisms. Of particular relevance, it also develops a deformable piecewise rigid atlas model which is regularized to preserve inter-observation distances between neighbors. These modeling considerations are shown to improve atlas construction and key-point alignment across a diversity of datasets with small sample sizes including neuron positions in C. elegans hermaphrodites, fluorescence microscopy of male C. elegans , and images of fruit fly wings. Code is accessible at https://github.com/amin-nejat/Deformable-Atlas. |
