napari/napari: 0.4.12rc2

Sofroniew, Nicholas; Lambert, Talley J.; Evans, Kira; Nunez-Iglesias, Juan; Bokota, Grzegorz; Peña-Castellanos, Gonzalo; Winston, Philip; Yamauchi, Kevin A.; Bussonnier, Matthias; Pop, Draga Doncila; Liu, Ziyang; Pam,; alisterburt,; Buckley, Genevieve; Sweet, Andy; Gaifas, Lorenzo; Rodríguez-Guerra, Jaime; Migas, Lukasz; Hilsenstein, Volker; Bragantini, Jordão; Lee, Gregory R.; Héctor,; Freeman, Jeremy L.; Boone, Peter; Lowe, Alan R.; Gohlke, Christoph; Royer, Loïc; Pierré, Andrea; Har-Gil, Hagai

doi:10.5281/zenodo.5587893

Cited by 5 publications

(7 citation statements)

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“…The STracking library simplifies the design of single particle tracking workflows through a graphical interface using napari and a comprehensive Python library of functions. Unlike previous single particle tracking tools in Python ecosystem, it provides a very flexible solution for processing and visualizing the tracks taking advantage of Napari (Sofroniew et al, 2021) viewer for 3D þ time representation. A similar approach was introduced in TrackMate software (Tinevez et al, 2017) for the visualization and validation of 2D tracks in Fiji (Schindelin et al, 2012) java-based environment.…”

Section: Discussionmentioning

confidence: 99%

“…Tracks features and split/merge events are stored using dictionaries. This data representation is the same as napari (Sofroniew et al, 2021) points and tracks layers, making STracking natively compatible with the napari viewer. We thus implemented a STracking napari plugin suite (napari-tracks-reader, naparistracking).…”

Section: Implementation and Applicationmentioning

confidence: 99%

“…Python tools for visualization [e.g. napari (Sofroniew et al, 2021), ipyvolume (Breddels et al, 2018), SeeVis (Hattab & Nattkemper, 2019)] and analysis [ZeroCostDL4Mic (von Chamier et al, 2021), BioImageIT (Prigent et al, 2021), Cellpose (Stringer et al, 2021)] are widely applied to microscopy images.…”

Section: Introductionmentioning

confidence: 99%

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STracking: a free and open-source Python library for particle tracking and analysis

et al. 2022

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Summary Analysis of intra and extra cellular dynamic like vesicles transport involves particle tracking algorithms. The design of a particle tracking pipeline is a routine but tedious task. Therefore, particle dynamics analysis is often performed by combining several pieces of software (filtering, detection, tracking…) requiring many manual operations, and thus leading to poorly reproducible results. Given the new segmentation tools based on deep learning, modularity and interoperability between software have become essential in particle tracking algorithms. A good synergy between a particle detector and a tracker is of paramount importance. In addition, a user-friendly interface to control the quality of estimated trajectories is necessary. To address these issues, we developed STracking, a python library that allows combining algorithms into standardized particle tracking pipelines. Availability and Implementation STracking is available as a python library using “pip install” and the source code is publicly available on GitHub (https://github.com/sylvainprigent/stracking). A graphical interface is available using two napari plugins: napari-stracking and napari-tracks-reader. These napari plugins can be installed via the napari plugins menu or using “pip install”. The napari plugin source codes are available on GitHub (https://github.com/sylvainprigent/napari-tracks-reader, https://github.com/sylvainprigent/napari-stracking). Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Discussionmentioning

confidence: 99%

Section: Implementation and Applicationmentioning

confidence: 99%

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STracking: a free and open-source Python library for particle tracking and analysis

et al. 2022

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“…Several smart visualization methods have been developed over the years with the aim to gain easiness in navigation such as the VTK viewer ( de Chaumont et al, 2012 ; Hanwell et al, 2015 ) or ClearVolume ( Royer et al, 2015 ). Some commercial (Imaris, Aivia) or recently published ( Leggio et al, 2019 ; Sofroniew et al, 2021 ), tools greatly improved navigation in imaging data sets ( Supplementary Figure S1 ; same dataset visualized within ClearVolume, napari, Imaris and Aivia). With one exception, and limited to 3D visualization, ConfocalVR ( Stefani et al, 2018 ), these approaches provide little or no support for VR/AR.…”

Section: Introductionmentioning

confidence: 99%

Challenges of intracellular visualization using virtual and augmented reality

et al. 2022

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Microscopy image observation is commonly performed on 2D screens, which limits human capacities to grasp volumetric, complex, and discrete biological dynamics. With the massive production of multidimensional images (3D + time, multi-channels) and derived images (e.g., restored images, segmentation maps, and object tracks), scientists need appropriate visualization and navigation methods to better apprehend the amount of information in their content. New modes of visualization have emerged, including virtual reality (VR)/augmented reality (AR) approaches which should allow more accurate analysis and exploration of large time series of volumetric images, such as those produced by the latest 3D + time fluorescence microscopy. They include integrated algorithms that allow researchers to interactively explore complex spatiotemporal objects at the scale of single cells or multicellular systems, almost in a real time manner. In practice, however, immersion of the user within 3D + time microscopy data represents both a paradigm shift in human-image interaction and an acculturation challenge, for the concerned community. To promote a broader adoption of these approaches by biologists, further dialogue is needed between the bioimaging community and the VR&AR developers.

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“…In addition, managing often massive datasets requires dedicated expertise in computer science to scale up storage and computational resources. Now, with the emergence of artificial intelligence, such as deep learning, in bioimaging (for example, ImJoy 5 ), the automation of processing tasks and the implementation of analysis pipelines that include image visualization (such as napari 6 ), it is necessary to consider all stages of the data life cycle and new human-machine interactions. It is worth noting that data handling must now meet high quality criteria that will ensure identification, accessibility and interoperability of data with their processing, storage and analysis.…”

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confidence: 99%

BioImageIT: Open-source framework for integration of image data management with analysis

et al. 2022

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BioImageIT source code used in this publication is open source and published under the BSD 4-Clause "Original" or "Old" License. The source code is available through GitHub: https://github.com/bioimageit. The latest stable releases used in this publication, and the current versions, which include bug fixes and updates, are available on the Zenodo repository (ref. 12 ). Details of how to use the software are described on the project's website: nature methods Correspondencehttps://bioimageit.github.io/. The Omero plugin is licensed under the GNU General Public License v2.0.

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napari/napari: 0.4.12rc2

Cited by 5 publications

References 0 publications

STracking: a free and open-source Python library for particle tracking and analysis

STracking: a free and open-source Python library for particle tracking and analysis

Challenges of intracellular visualization using virtual and augmented reality

BioImageIT: Open-source framework for integration of image data management with analysis

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