In Bioinformatics (Oxford, England)
MOTIVATION : Light field microscopy is a compact solution to high-speed 3D fluorescence imaging. Usually, we need to do 3D deconvolution to the captured raw data. Although there are deep neural network methods that can accelerate the reconstruction process, the model is not universally applicable for all system parameters. Here, we develop AutoDeconJ, a GPU accelerated ImageJ plugin for 4.4x faster and accurate deconvolution of light field microscopy data. We further propose an image quality metric for the deconvolution process, aiding in automatically determining the optimal number of iterations with higher reconstruction accuracy and fewer artifacts.
RESULTS : Our proposed method outperforms state-of-the-art light field deconvolution methods in reconstruction time and optimal iteration numbers prediction capability. It shows better universality of different light field point spread function (PSF) parameters than the deep learning method. The fast, accurate, and general reconstruction performance for different PSF parameters suggests its potential for mass 3D reconstruction of light field microscopy data.
AVAILABILITY AND IMPLEMENTATION : The codes, the documentation, and example data are available on an open source at: https://github.com/Onetism/AutoDeconJ.git.
SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.
Su Changqing, Gao Yuhan, Zhou You, Sun Yaoqi, Yan Chenggang, Yin Haibing, Xiong Bo
2022-Nov-28
