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Simplifying deep learning to enhance accessibility of large-scale 3D brain imaging analysis

We created DELiVR, a deep-learning pipeline for 3D brain-cell mapping that is trained with virtual reality-generated reference annotations. It can be deployed via the user-friendly interface of the open-source software Fiji, which makes the analysis of large-scale 3D brain images widely accessible to scientists without computational expertise.

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Fig. 1: Deep-learning-based mapping of brain cells with DELiVR.

References

  1. Molbay, M. et al. A guidebook for DISCO tissue clearing. Mol. Syst. Biol. 17, e9807 (2021). A review that presents an overview of tissue clearing and LSFM.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Renier, N. et al. Mapping of brain activity by automated volume analysis of immediate early genes. Cell 165, 1789–1802 (2016). This paper presents ClearMap, a threshold-based method for brain activity mapping.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Al-Maskari, R. et al. On the pitfalls of deep image segmentation for lightsheet microscopy. In Medical Imaging with Deep Learning https://openreview.net/forum?id=3Krfu84W-Wx (2022). This short review summarizes challenges for segmenting structures imaged through LSFM.

  4. Pidhorskyi, S. et al. syGlass: Interactive exploration of multidimensional images using virtual reality head-mounted displays. Preprint at https://doi.org/10.48550/arXiv.1804.08197 (2018). This paper describes the development of a software package for visualizing volumetric data with VR headsets.

  5. Todorov, M. I. et al. Machine learning analysis of whole mouse brain vasculature. Nat. Methods. 17, 442–449 (2020). This paper presents a machine learning-based approach for the segmentation of the entire vasculature in whole mouse brains.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

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This is a summary of: Kaltenecker, D. et al. Virtual reality-empowered deep-learning analysis of brain cells. Nat. Methods https://doi.org/10.1038/s41592-024-02245-2 (2024).

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Simplifying deep learning to enhance accessibility of large-scale 3D brain imaging analysis. Nat Methods (2024). https://doi.org/10.1038/s41592-024-02246-1

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