On the use of image quality measures of multi-views in light sheet fluorescence 3D microscopy

In this report, we discuss the interest of quality metrics for imaging and image processing of multi-views in light sheet fluorescent 3D microscopy. Various metrics of focus are tested on real and simulated data so as to automatically assess the informational quality of the images. Application of such metrics are given for several information tasks including online control of acquisition, fast registration or image fusion. Illustrations are given for typical samples of interest for in vivo imaging with light sheet microscopy such as spheroids or organoids. We point to the reader softwares freely available under FIJI which enable to test the computation of a basic quality metric, for registration and fusion.

[1]  Tobias Pietzsch,et al.  BigDataViewer: visualization and processing for large image data sets , 2015, Nature Methods.

[2]  Stephan Saalfeld,et al.  Software for bead-based registration of selective plane illumination microscopy data , 2010, Nature Methods.

[3]  K. Deisseroth,et al.  Advanced CLARITY for rapid and high-resolution imaging of intact tissues , 2014, Nature Protocols.

[4]  Domenec Puig,et al.  Analysis of focus measure operators for shape-from-focus , 2013, Pattern Recognit..

[5]  Jean-Christophe Olivo-Marin,et al.  Imaging tissue-mimic with light sheet microscopy: A comparative guideline , 2017, Scientific Reports.

[6]  Tobias Pietzsch,et al.  An automated workflow for parallel processing of large multiview SPIM recordings , 2015, Bioinform..

[7]  J. Huisken,et al.  Light Sheet Microscopy , 2018 .

[8]  J. Huisken,et al.  The smart and gentle microscope , 2015, Nature Biotechnology.

[9]  Fernando Amat,et al.  Efficient processing and analysis of large-scale light-sheet microscopy data , 2015, Nature Protocols.

[10]  François de Vieilleville,et al.  Alternating direction method of multipliers applied to 3D light sheet fluorescence microscopy image deblurring using GPU hardware , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Kei Ito,et al.  Image processing for precise three-dimensional registration and stitching of thick high-resolution laser-scanning microscopy image stacks , 2018, Comput. Biol. Medicine.

[12]  Stephan Preibisch,et al.  Efficient Bayesian-based multiview deconvolution , 2013, Nature Methods.

[13]  Lars Hufnagel,et al.  Multiview light-sheet microscope for rapid in toto imaging , 2012, Nature Methods.

[14]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.