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Resources » Paper

Nejatbakhsh A et al. (2023) Inf Process Med Imaging "Learning Probabilistic Piecewise Rigid Atlases of Model Organisms via Generative Deep Networks."

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  • Comments on Nejatbakhsh A et al. (2023) Inf Process Med Imaging "Learning Probabilistic Piecewise Rigid Atlases of Model Organisms via Generative Deep Networks." (0)

  • Overview

    PMID:
    37476079
    DOI:
    10.1007/978-3-031-34048-2_26
    Status:
    Publication type:
    Journal_article
    WormBase ID:
    WBPaper00066053

    Nejatbakhsh A, Dey N, Venkatachalam V, Yemini E, Paninski L, & Varol E (2023). Learning Probabilistic Piecewise Rigid Atlases of Model Organisms via Generative Deep Networks. Inf Process Med Imaging, 13939, 332-343. doi:10.1007/978-3-031-34048-2_26

    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 <i>C. elegans</i> 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 <i>C. elegans</i> hermaphrodites, fluorescence microscopy of male <i>C. elegans</i>, and images of fruit fly wings. Code is accessible at https://github.com/amin-nejat/Deformable-Atlas.


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