Image processing for precise three-dimensional registration and stitching of thick high-resolution laser-scanning microscopy image stacks

The possible depth of imaging of laser-scanning microscopy is limited not only by the working distances of objective lenses but also by image degradation caused by attenuation and diffraction of light passing through the specimen. To tackle this problem, one can either flip the sample to record imag...

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Vydáno v:	Computers in biology and medicine Ročník 92; s. 22 - 41
Hlavní autoři:	Murtin, Chloé, Frindel, Carole, Rousseau, David, Ito, Kei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Ltd 01.01.2018 Elsevier Limited Elsevier
Témata:	Algorithms Animals Axes (reference lines) Bioengineering Brain Brain - cytology Brain - diagnostic imaging Computer Simulation Connectomics Dimensional stability Drosophila Drosophila - anatomy & histology Drosophila - cytology Engineering Sciences Feasibility studies Feature extraction Image acquisition Image degradation Image processing Image resolution Imaging Imaging, Three-Dimensional - methods Insects Internal Medicine International conferences Laser-scanning microscopy Lasers Life Sciences Light diffraction Matching Medical imaging Methods Microscopy Microscopy, Confocal - methods Microtools Neuroimaging Neurons Noise Noise levels Other Registration Scanning microscopy SIFT Signal and Image processing Software Stacks Stitching Translation Japan Laser-scanning microscopy Brain SIFT Registration Drosophila Stitching Connectomics
ISSN:	0010-4825, 1879-0534, 1879-0534
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Abstract	The possible depth of imaging of laser-scanning microscopy is limited not only by the working distances of objective lenses but also by image degradation caused by attenuation and diffraction of light passing through the specimen. To tackle this problem, one can either flip the sample to record images from both sides of the specimen or consecutively cut off shallow parts of the sample after taking serial images of certain thickness. Multiple image substacks acquired in these ways should be combined afterwards to generate a single stack. However, subtle movements of samples during image acquisition cause mismatch not only in the translation along x-, y-, and z-axes and rotation around z-axis but also tilting around x- and y-axes, making it difficult to register the substacks precisely. In this work, we developed a novel approach called 2D-SIFT-in-3D-Space using Scale Invariant Feature Transform (SIFT) to achieve robust three-dimensional matching of image substacks. Our method registers the substacks by separately fixing translation and rotation along x-, y-, and z-axes, through extraction and matching of stable features across two-dimensional sections of the 3D stacks. To validate the quality of registration, we developed a simulator of laser-scanning microscopy images to generate a virtual stack in which noise levels and rotation angles are controlled with known parameters. We illustrate quantitatively the performance of our approach by registering an entire brain of Drosophila melanogaster consisting of 800 sections. Our approach is also demonstrated to be extendable to other types of data that share large dimensions and need of fine registration of multiple image substacks. This method is implemented in Java and distributed as ImageJ/Fiji plugin. The source code is available via Github (http://www.creatis.insa-lyon.fr/site7/fr/MicroTools). [Display omitted] •A novel approach using SIFT to achieve robust three-dimensional matching of image substacks is proposed.•We illustrate quantitatively the performance of our approach by registering an entire brain of Drosophila melanogaster.•Our approach is extendable to other data that share large dimensions and need of fine registration of multiple image.•This method is implemented in Java and freely distributed as ImageJ/Fiji plugin.
AbstractList	AbstractThe possible depth of imaging of laser-scanning microscopy is limited not only by the working distances of objective lenses but also by image degradation caused by attenuation and diffraction of light passing through the specimen. To tackle this problem, one can either flip the sample to record images from both sides of the specimen or consecutively cut off shallow parts of the sample after taking serial images of certain thickness. Multiple image substacks acquired in these ways should be combined afterwards to generate a single stack. However, subtle movements of samples during image acquisition cause mismatch not only in the translation along x-, y-, and z-axes and rotation around z-axis but also tilting around x- and y-axes, making it difficult to register the substacks precisely. In this work, we developed a novel approach called 2D-SIFT-in-3D-Space using Scale Invariant Feature Transform (SIFT) to achieve robust three-dimensional matching of image substacks. Our method registers the substacks by separately fixing translation and rotation along x-, y-, and z-axes, through extraction and matching of stable features across two-dimensional sections of the 3D stacks. To validate the quality of registration, we developed a simulator of laser-scanning microscopy images to generate a virtual stack in which noise levels and rotation angles are controlled with known parameters. We illustrate quantitatively the performance of our approach by registering an entire brain of Drosophila melanogaster consisting of 800 sections. Our approach is also demonstrated to be extendable to other types of data that share large dimensions and need of fine registration of multiple image substacks. This method is implemented in Java and distributed as ImageJ/Fiji plugin. The source code is available via Github ( http://www.creatis.insa-lyon.fr/site7/fr/MicroTools). The possible depth of imaging of laser-scanning microscopy is limited not only by the working distances of objective lenses but also by image degradation caused by attenuation and diffraction of light passing through the specimen. To tackle this problem, one can either flip the sample to record images from both sides of the specimen or consecutively cut off shallow parts of the sample after taking serial images of certain thickness. Multiple image substacks acquired in these ways should be combined afterwards to generate a single stack. However, subtle movements of samples during image acquisition cause mismatch not only in the translation along x-, y-, and z-axes and rotation around z-axis but also tilting around x- and y-axes, making it difficult to register the substacks precisely. In this work, we developed a novel approach called 2D-SIFT-in-3D-Space using Scale Invariant Feature Transform (SIFT) to achieve robust three-dimensional matching of image substacks. Our method registers the substacks by separately fixing translation and rotation along x-, y-, and z-axes, through extraction and matching of stable features across two-dimensional sections of the 3D stacks. To validate the quality of registration, we developed a simulator of laser-scanning microscopy images to generate a virtual stack in which noise levels and rotation angles are controlled with known parameters. We illustrate quantitatively the performance of our approach by registering an entire brain of Drosophila melanogaster consisting of 800 sections. Our approach is also demonstrated to be extendable to other types of data that share large dimensions and need of fine registration of multiple image substacks. This method is implemented in Java and distributed as ImageJ/Fiji plugin. The source code is available via Github (http://www.creatis.insa-lyon.fr/site7/fr/MicroTools). The possible depth of imaging of laser-scanning microscopy is limited not only by the working distances of objective lenses but also by image degradation caused by attenuation and diffraction of light passing through the specimen. To tackle this problem, one can either flip the sample to record images from both sides of the specimen or consecutively cut off shallow parts of the sample after taking serial images of certain thickness. Multiple image substacks acquired in these ways should be combined afterwards to generate a single stack. However, subtle movements of samples during image acquisition cause mismatch not only in the translation along x-, y-, and z-axes and rotation around z-axis but also tilting around x- and y-axes, making it difficult to register the substacks precisely. In this work, we developed a novel approach called 2D-SIFT-in-3D-Space using Scale Invariant Feature Transform (SIFT) to achieve robust three-dimensional matching of image substacks. Our method registers the substacks by separately fixing translation and rotation along x-, y-, and z-axes, through extraction and matching of stable features across two-dimensional sections of the 3D stacks. To validate the quality of registration, we developed a simulator of laser-scanning microscopy images to generate a virtual stack in which noise levels and rotation angles are controlled with known parameters. We illustrate quantitatively the performance of our approach by registering an entire brain of Drosophila melanogaster consisting of 800 sections. Our approach is also demonstrated to be extendable to other types of data that share large dimensions and need of fine registration of multiple image substacks. This method is implemented in Java and distributed as ImageJ/Fiji plugin. The source code is available via Github (http://www.creatis.insa-lyon.fr/site7/fr/MicroTools).The possible depth of imaging of laser-scanning microscopy is limited not only by the working distances of objective lenses but also by image degradation caused by attenuation and diffraction of light passing through the specimen. To tackle this problem, one can either flip the sample to record images from both sides of the specimen or consecutively cut off shallow parts of the sample after taking serial images of certain thickness. Multiple image substacks acquired in these ways should be combined afterwards to generate a single stack. However, subtle movements of samples during image acquisition cause mismatch not only in the translation along x-, y-, and z-axes and rotation around z-axis but also tilting around x- and y-axes, making it difficult to register the substacks precisely. In this work, we developed a novel approach called 2D-SIFT-in-3D-Space using Scale Invariant Feature Transform (SIFT) to achieve robust three-dimensional matching of image substacks. Our method registers the substacks by separately fixing translation and rotation along x-, y-, and z-axes, through extraction and matching of stable features across two-dimensional sections of the 3D stacks. To validate the quality of registration, we developed a simulator of laser-scanning microscopy images to generate a virtual stack in which noise levels and rotation angles are controlled with known parameters. We illustrate quantitatively the performance of our approach by registering an entire brain of Drosophila melanogaster consisting of 800 sections. Our approach is also demonstrated to be extendable to other types of data that share large dimensions and need of fine registration of multiple image substacks. This method is implemented in Java and distributed as ImageJ/Fiji plugin. The source code is available via Github (http://www.creatis.insa-lyon.fr/site7/fr/MicroTools). The possible depth of imaging of laser-scanning microscopy is limited not only by the working distances of objective lenses but also by image degradation caused by attenuation and diffraction of light passing through the specimen. To tackle this problem, one can either flip the sample to record images from both sides of the specimen or consecutively cut off shallow parts of the sample after taking serial images of certain thickness. Multiple image substacks acquired in these ways should be combined afterwards to generate a single stack. However, subtle movements of samples during image acquisition cause mismatch not only in the translation along x-, y-, and z-axes and rotation around z-axis but also tilting around x- and y-axes, making it difficult to register the substacks precisely. In this work, we developed a novel approach called 2D-SIFT-in-3D-Space using Scale Invariant Feature Transform (SIFT) to achieve robust three-dimensional matching of image substacks. Our method registers the substacks by separately fixing translation and rotation along x-, y-, and z-axes, through extraction and matching of stable features across two-dimensional sections of the 3D stacks. To validate the quality of registration, we developed a simulator of laser-scanning microscopy images to generate a virtual stack in which noise levels and rotation angles are controlled with known parameters. We illustrate quantitatively the performance of our approach by registering an entire brain of Drosophila melanogaster consisting of 800 sections. Our approach is also demonstrated to be extendable to other types of data that share large dimensions and need of fine registration of multiple image substacks. This method is implemented in Java and distributed as ImageJ/Fiji plugin. The source code is available via Github (http://www.creatis.insa-lyon.fr/site7/fr/MicroTools). [Display omitted] •A novel approach using SIFT to achieve robust three-dimensional matching of image substacks is proposed.•We illustrate quantitatively the performance of our approach by registering an entire brain of Drosophila melanogaster.•Our approach is extendable to other data that share large dimensions and need of fine registration of multiple image.•This method is implemented in Java and freely distributed as ImageJ/Fiji plugin. The possible depth of imaging of laser-scanning microscopy is limited not only by the working distances of objective lenses but also by image degradation caused by attenuation and diffraction of light passing through the specimen. To tackle this problem, one can either flip the sample to record images from both sides of the specimen or consecutively cut off shallow parts of the sample after taking serial images of certain thickness. Multiple image substacks acquired in these ways should be combined afterwards to generate a single stack. However, subtle movements of samples during image acquisition cause mismatch not only in the translation along x-, y-, and z-axes and rotation around z-axis but also tilting around x-and y-axes, making it difficult to register the substacks precisely. In this work, we developed a novel approach called 2D-SIFT-in-3D-Space using Scale Invariant Feature Transform (SIFT) to achieve robust three-dimensional matching of image substacks. Our method registers the substacks by separately fixing translation and rotation along x-, y-, and z-axes, through extraction and matching of stable features across two-dimensional sections of the 3D stacks. To validate the quality of registration, we developed a simulator of laser-scanning microscopy images to generate a virtual stack in which noise levels and rotation angles are controlled with known parameters. We illustrate quantitatively the performance of our approach by registering an entire brain of Drosophila melanogaster consisting of 800 sections. Our approach is also demonstrated to be extendable to other types of data that share large dimensions and need of fine registration of multiple image substacks. This method is implemented in Java and distributed as ImageJ/Fiji plugin. The source code is available via Github (www.creatis.insa-lyon.fr/site7/fr/MicroTools).
Author	Ito, Kei Frindel, Carole Murtin, Chloé Rousseau, David
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CitedBy_id	crossref_primary_10_1007_s00709_021_01679_1 crossref_primary_10_1093_gigascience_giaa035 crossref_primary_10_3389_feart_2020_00345 crossref_primary_10_1063_5_0233070 crossref_primary_10_1371_journal_pone_0214504 crossref_primary_10_1002_pro_3993
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Keywords	Laser-scanning microscopy Brain SIFT Registration Drosophila Stitching Connectomics
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Snippet	The possible depth of imaging of laser-scanning microscopy is limited not only by the working distances of objective lenses but also by image degradation... AbstractThe possible depth of imaging of laser-scanning microscopy is limited not only by the working distances of objective lenses but also by image...
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SubjectTerms	Algorithms Animals Axes (reference lines) Bioengineering Brain Brain - cytology Brain - diagnostic imaging Computer Simulation Connectomics Dimensional stability Drosophila Drosophila - anatomy & histology Drosophila - cytology Engineering Sciences Feasibility studies Feature extraction Image acquisition Image degradation Image processing Image resolution Imaging Imaging, Three-Dimensional - methods Insects Internal Medicine International conferences Laser-scanning microscopy Lasers Life Sciences Light diffraction Matching Medical imaging Methods Microscopy Microscopy, Confocal - methods Microtools Neuroimaging Neurons Noise Noise levels Other Registration Scanning microscopy SIFT Signal and Image processing Software Stacks Stitching Translation
Title	Image processing for precise three-dimensional registration and stitching of thick high-resolution laser-scanning microscopy image stacks
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