napari sairyscan

napari-sairyscan

Airyscan image reconstruction


    This plugin implements various methods to reconstruct high resolution images for the Airyscan microscope raw data

    Description

    Airyscan raw images are confocal images obtained from 32 sub-detectors. Several methods can be used to reconstruct a high resolution image from these 32 sub-detectors images. This plugin implements the following methods:

    • Pseudo-confocal: creates a pseudo confocal image by summing 7, 19 or 19 detectors
    • ISM: creates a higher resolution image by summing the images from the 32 sub-detectors after co-registering all the images to the central detector. A deconvolution algorithm can be applied in post-processing to gain more resolution
    • IFED: reconstructs a high resolution image by subtracting the outer ring detector to the central detector. This method can be interpreted as a 'virtual STED'
    • ISFED: reconstructs a high resolution image by combining the co-registered detectors images and the raw detectors images
    • Join deconvolution: reconstructs a high resolution image by jointly deblurring all 32 detectors with a variational approach.

    Example image

    Intended Audience & Supported Data

    Supported data are raw images from the Airyscan microscope. These images must be stacks of 32 layers corresponding to the 32 detectors. The Airyscan reader plugin can open .czi raw files. Data can also be stored in any format that napari can open.

    Quickstart

    • Open the sample image from the menu File > Open samples > napari-sairyscan > SAiryscan

    Open image Open image

    • Open the SAiryscan plugin from the menu Plugins > napari-sairyscan: Airyscan reconstruction

    Open image Open image

    • We select the join deconvolution method and run it with the default parameters. Default parameters are optimized for the sample image:

    Open image

    Getting Help

    For any bug report or feature request please file an issue

    How to Cite

    If you use this plugin please cite the paper:

    @INPROCEEDINGS{9054640,
    author={Prigent, Sylvain and Dutertre, Stephanie and Kervrann, Charles},
    booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
    title={Empirical Sure-Guided Microscopy Super-Resolution Image Reconstruction from Confocal Multi-Array Detectors}, 
    year={2020},
    volume={},
    number={},
    pages={1075-1079},
    doi={10.1109/ICASSP40776.2020.9054640}}

    Version:

    • 0.0.2

    Release date:

    • 07 June 2022

    First released:

    • 03 June 2022

    License:

    • BSD-3-Clause

    Supported data:

    • Information not submitted

    Plugin type:

    Open extension:

    GitHub activity:

    • Stars: 0
    • Forks: 0
    • Issues + PRs: 0

    Python versions supported:

    Operating system:

    Requirements:

    • numpy
    • magicgui
    • qtpy
    • sairyscan (>=0.0.2)

    Sign up to receive updates