Isotropic reconstruction of 3D fluorescence microscopy images using convolutional neural networks

@article{Weigert2017IsotropicRO,
  title={Isotropic reconstruction of 3D fluorescence microscopy images using convolutional neural networks},
  author={Martin Weigert and Lo{\"i}c Royer and Florian Jug and Eugene W. Myers},
  journal={ArXiv},
  year={2017},
  volume={abs/1704.01510}
}
  • Martin Weigert, Loïc Royer, +1 author Eugene W. Myers
  • Published 2017
  • Computer Science
  • ArXiv
  • Fluorescence microscopy images usually show severe anisotropy in axial versus lateral resolution. This hampers downstream processing, i.e. the automatic extraction of quantitative biological data. While deconvolution methods and other techniques to address this problem exist, they are either time consuming to apply or limited in their ability to remove anisotropy. We propose a method to recover isotropic resolution from readily acquired anisotropic data. We achieve this using a convolutional… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 17 REFERENCES

    U-Net: Convolutional Networks for Biomedical Image Segmentation

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    Image Super-Resolution Using Deep Convolutional Networks

    VIEW 1 EXCERPT

    Accurate Image Super-Resolution Using Very Deep Convolutional Networks

    VIEW 1 EXCERPT

    Efficient Bayesian-based multiview deconvolution

    VIEW 2 EXCERPTS