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| 265 ++++++++++++++++++++ 1 file changed, 265 insertions(+) create mode 100644 joss.07079/10.21105.joss.07079.crossref.xml diff --git a/joss.07079/10.21105.joss.07079.crossref.xml b/joss.07079/10.21105.joss.07079.crossref.xml new file mode 100644 index 0000000000..b40284c043 --- /dev/null +++ b/joss.07079/10.21105.joss.07079.crossref.xml @@ -0,0 +1,265 @@ + + + + 20240923142351-eb1f776a67e156ea5c9c87b5c0952ba6e8b8410d + 20240923142351 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 09 + 2024 + + + 9 + + 101 + + + + PyStack3D: A python package for fast image stack +correction + + + + Patrick + Quéméré + https://orcid.org/0009-0008-6936-1249 + + + Thomas + David + https://orcid.org/0000-0002-6796-220X + + + + 09 + 23 + 2024 + + + 7079 + + + 10.21105/joss.07079 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.13827928 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/7079 + + + + 10.21105/joss.07079 + https://joss.theoj.org/papers/10.21105/joss.07079 + + + https://joss.theoj.org/papers/10.21105/joss.07079.pdf + + + + + + Fiji: An open-source platform for +biological-image analysis + Schindelin + Nature Methods + 9 + 10.1038/nmeth.2019 + 2012 + Schindelin, J., Arganda-Carreras, I., +& Frise, E. (2012). Fiji: An open-source platform for +biological-image analysis. Nature Methods, 9, 676–682. +https://doi.org/10.1038/nmeth.2019 + + + NIH image to ImageJ: 25 years of image +analysis + Schneider + Nature Methods + 7 + 9 + 10.1038/nmeth.2089 + 2012 + Schneider, C. A., Rasband, W. S., +& Eliceiri, K. W. (2012). NIH image to ImageJ: 25 years of image +analysis. Nature Methods, 9(7), 671–675. +https://doi.org/10.1038/nmeth.2089 + + + A pyramid approach to subpixel registration +based on intensity + Thevenaz + IEEE Transactions on Image +Processing + 1 + 7 + 10.1109/83.650848 + 1998 + Thevenaz, P., Ruttiman, U. E., & +Unser, M. (1998). A pyramid approach to subpixel registration based on +intensity. IEEE Transactions on Image Processing, 7(1), 27–41. +https://doi.org/10.1109/83.650848 + + + PyVSNR 2.0.0 + Pavy + 10.5281/zenodo.10623640 + 2024 + Pavy, K., & Quéméré, P. (2024). +PyVSNR 2.0.0 (Version 2.0.0). Zenodo. +https://doi.org/10.5281/zenodo.10623640 + + + Variational algorithms to remove stationary +noise. Application to microscopy imaging. + Fehrenbach + IEEE Image Processing + 10 + 21 + 10.1109/TIP.2012.2206037 + 2012 + Fehrenbach, J., Weiss, W., & +Lorenzo, C. (2012). Variational algorithms to remove stationary noise. +Application to microscopy imaging. IEEE Image Processing, 21(10), +4420–4430. +https://doi.org/10.1109/TIP.2012.2206037 + + + Roadmap for focused ion beam +technologies + Höflich + Applied Physics Reviews + 4 + 10 + 10.1063/5.0162597 + 2023 + Höflich, K., Hobler, G., Allen, F. +I., Wirtz, T., Rius, G., McElwee-White, L., Krasheninnikov, A. V., +Schmidt, M., Utke, I., Klingner, N., & others. (2023). Roadmap for +focused ion beam technologies. Applied Physics Reviews, 10(4). +https://doi.org/10.1063/5.0162597 + + + Classification of FIB/SEM-tomography images +for highly porous multiphase materials using random forest +classifiers + Osenberg + Journal of Power Sources + 570 + 10.1016/j.jpowsour.2023.233030 + 0378-7753 + 2023 + Osenberg, M., Hilger, A., Neumann, +M., Wagner, A., Bohn, N., Binder, J. R., Schmidt, V., Banhart, J., & +Manke, I. (2023). Classification of FIB/SEM-tomography images for highly +porous multiphase materials using random forest classifiers. Journal of +Power Sources, 570, 233030. +https://doi.org/10.1016/j.jpowsour.2023.233030 + + + High-fidelity image restoration of large 3D +electron microscopy volume + Kreinin + bioRxiv + 10.1101/2023.09.14.557785 + 2023 + Kreinin, Y., Gunn, P., Chklovskii, +D., & Wu, J. (2023). 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Optics Express, 17(10), 8567–8591. +https://doi.org/10.1364/OE.17.008567 + + + Cryo-FIB-SEM as a promising tool for +localizing proteins in 3D + Spehner + Journal of Structural Biology + 1 + 211 + 10.1016/j.jsb.2020.107528 + 1047-8477 + 2020 + Spehner, D., Steyer, A. M., +Bertinetti, L., Orlov, I., Benoit, L., Pernet-Gallay, K., Schertel, A., +& Schultz, P. (2020). Cryo-FIB-SEM as a promising tool for +localizing proteins in 3D. Journal of Structural Biology, 211(1), +107528. +https://doi.org/10.1016/j.jsb.2020.107528 + + + PolishEM: image enhancement in +FIB–SEM + Fernandez + Bioinformatics + 12 + 36 + 10.1093/bioinformatics/btaa218 + 1367-4803 + 2020 + Fernandez, J.-J., Torres, T. E., +Martin-Solana, E., Goya, G. F., & Fernandez-Fernandez, M.-R. (2020). +PolishEM: image enhancement in FIB–SEM. 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    + + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +7079 +10.21105/joss.07079 + +PyStack3D: A python package for fast image stack +correction + + + +https://orcid.org/0009-0008-6936-1249 + +Quéméré +Patrick + + + + +https://orcid.org/0000-0002-6796-220X + +David +Thomas + + + + + +Univ. Grenoble Alpes, CEA, Leti, Grenoble, +France + + + + +Univ. Grenoble Alpes, CEA, LITEN, Grenoble, +France + + + + +19 +3 +2024 + +9 +101 +7079 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +stack +image processing +correction +cropping +background removal +registration +intensity rescaling +destriping +curtaining +resampling +multithreading +multiprocessing +FIB-SEM +TOF-SIMS + + + + + + Summary +

    Three-dimensional reconstruction from 2D image stacks is a crucial + technique in various scientific domains. For instance, acquisition + techniques like Focused Ion Beam Scanning Electron Microscopy + (FIB-SEM) leverage this approach to visualize complex structures at + the nanoscale. However, creating a “clean” 3D stack often requires + image corrections to remove artifacts and inconsistencies, + particularly for volume segmentation, a crucial process for 3D + quantitative data analysis.

    +

    Here we present PyStack3D, a Python + open-source library, that aims at performing several image ‘cleaning’ + tasks + ([fig:PyStack3D]) + in the most integrated and efficient manner possible.

    + +

    a) Synthetic stack with different types of defects and + related processing. b) Corrected stack with + PyStack3D. c) Ground + truth.

    + +
    +
    + + Statement of need +

    Accurate 3D reconstruction is crucial for extracting detailed + features across various imaging techniques. In life + sciences, for instance, this includes identifying cellular + organelles, understanding tissue architecture or studying protein + localization. In energy materials, precise imaging is + necessary for analyzing porous structures, mapping catalyst particles + or assessing battery electrode interfaces. Various imaging methods, + such as confocal microscopy, light sheet microscopy, and electron + tomography, often introduce distortions or misalignments due to + factors like optical aberrations, sample movement or inconsistent + illumination. These issues become even more pronounced with FIB-SEM + (Höflich + et al., 2023), where artifacts from the milling process and + variations in sample preparation can further complicate the 3D + stack.

    +

    Effective correction of these distortions is essential for reliable + segmentation and accurate feature extraction + (Osenberg + et al., 2023; + Spehner + et al., 2020).

    +
    + + Statement of field +

    Certainly, one of the most widely used open-source software for + performing image stack corrections is the Fiji software + (Schindelin + et al., 2012), a distribution of ImageJ + (Schneider + et al., 2012). Written in Java, this software + offers numerous macros for the analysis and processing of 2D and 3D + images. Unfortunately, not all the macros needed to perform the stack + corrections exist, and the existing macros do not all support + multiprocessing, which can lead to processing times of several hours + for stacks composed of thousands of images (see + Appendix).

    +

    As an alternative, codes written in Python like + Hifiem + (Kreinin + et al., 2023), PolishEM + (Fernandez + et al., 2020) or Napari + (Sofroniew + et al., 2024) have been developed in recent years to achieve + processing times of just a few minutes thanks to multiprocessing + capabilities. PyStack3D, whose project started + in 2020, is part of this trend. Designed to be executed as a workflow, + PyStack3D aims to enable users to easily manage + the automation of such workflows. With the quickly obtained results, + users can easily readjust the parameters, and restart the processing + if needed.

    +
    + + Implementation +

    In PyStack3D, to reduce the memory + footprint, images (called “slices”) are loaded and processed one by + one either on a single processor or across multiple processors, + depending on the user’s machine capabilities.

    +

    The PyStack3D workflow is made up of + multiple processing steps, specified in a .toml + parameter file, and executed in the order desired by the user.

    +

    The processing steps currently offered by + PyStack3D are:

    + + +

    cropping to reduce the image field of view to the + user’s ROI (Region Of Interest)

    +
    + +

    background removal to reduce, from 2D or 3D + polynomial approximations, large-scaled brightness and contrast + variations issued for instance from shadowing or charging effects + in FIB-SEM images acquisition

    +
    + +

    intensity rescaling to homogenize the ‘gray’ + intensity distribution between successive slices and smooth out + abrupt intensity jumps that can occur due to, for instance, + variations in the beam source.

    +
    + +

    registration to correct the image misalignment due + to shifting, drift, rotation, … during the image acquisition + (based on the PyStackReg package, + Thevenaz + et al., 1998)

    +
    + +

    destriping to minimize artefacts like stripes or + curtain effects typically found in FIB-SEM images, based on the + PyVSNR package + (Fehrenbach + et al., 2012; + Pavy + & Quéméré, 2024), or wavelet decomposition + (Münch + et al., 2009)

    +
    + +

    resampling to correct non-uniform spatial + inter-slice distances and enable correct 3D volume + reconstructions

    +
    + +

    final cropping to eliminate artefacts potentially + produced near the edges during the image processing or to select + another ROI at the end.

    +
    +
    +

    At the end of each process step, PyStack3D + provides statistical profiles like evolution of minimum, maximum, and + mean values for each slice, and relevant visualizations specific to + the processing performed. In addition, 3D and 2D plots (cut-planes) + akin to those shown in + [fig:PyStack3D] + and + [fig:workflow], + respectively, can be produced.

    +

    Note that the processing can be carried out on multiple channels + corresponding to images issued from multiple detectors, typically + useful in the context of FIB-SEM input data. Moreover, when working + with a Zeiss microscope, some metadata issued from the equipment can + be automatically incorporated in the input + .toml parameter file.

    +

    In conclusion, Pystack3D has been designed + to evolve over time and accommodate new process steps. Its code + structure has been crafted to seamlessly integrate new + functionalities, leveraging multiprocessing capabilities.

    +
    + + Acknowledgements +

    This work, carried out on the CEA-Platform for Nanocharacterisation + (PFNC), was supported by the ``Recherche Technologique de Base’’ + program of the French National Research Agency (ANR).

    + + +

    Cut-planes related to the different process steps + applied to the stack presented in the + [fig:PyStack3D].

    + +
    +
    + + Appendix +

    Processing time for a stack composed of 2000 slices + (from + ex_real_stack_perf.py)

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Process stepFiji (s)PyStack3D (s)
    cropping75030
    bkg_removal (2D / 3D)250 / -70 / 40*
    destriping22400700**
    registration540025
    intensity_rescaling-25
    resampling-10
    +
    +

    image size: 4224 x 4224 before cropping / 2000 x 2000 + after cropping.

    +

    Machine: Linux - 32 CPUs Intel(R) Xeon(R) + Platinum 8362 CPU @ 2.80GHz.

    +

    (*) in 3D the polynomial coefficients are calculated only once, + unlike in 2D, where the coefficients are recalculated for each + slice.

    +

    (**) 120s with a GPU Nvidia A-100.

    +
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