Literature DB >> 34370407

New Extensibility and Scripting Tools in the ImageJ Ecosystem.

Niklas A Gahm1,2,3, Curtis T Rueden1, Edward L Evans1,3, Gabriel Selzer1, Mark C Hiner1, Jenu V Chacko1, Dasong Gao1, Nathan M Sherer4,5,6, Kevin W Eliceiri1,2,3,5,7.   

Abstract

ImageJ provides a framework for image processing across scientific domains while being fully open source. Over the years ImageJ has been substantially extended to support novel applications in scientific imaging as they emerge, particularly in the area of biological microscopy, with functionality made more accessible via the Fiji distribution of ImageJ. Within this software ecosystem, work has been done to extend the accessibility of ImageJ to utilize scripting, macros, and plugins in a variety of programming scenarios, e.g., from Groovy and Python and in Jupyter notebooks and cloud computing. We provide five protocols that demonstrate the extensibility of ImageJ for various workflows in image processing. We focus first on Fluorescence Lifetime Imaging Microscopy (FLIM) data, since this requires significant processing to provide quantitative insights into the microenvironments of cells. Second, we show how ImageJ can now be utilized for common image processing techniques, specifically image deconvolution and inversion, while highlighting the new, built-in features of ImageJ-particularly its capacity to run completely headless and the Ops matching feature that selects the optimal algorithm for a given function and data input, thereby enabling processing speedup. Collectively, these protocols can be used as a basis for automating biological image processing workflows.
© 2021 Wiley Periodicals LLC. Basic Protocol 1: Using PyImageJ for FLIM data processing Alternate Protocol: Groovy FLIMJ in Jupyter Notebooks Basic Protocol 2: Using ImageJ Ops for image deconvolution Support Protocol 1: Using ImageJ Ops matching feature for image inversion Support Protocol 2: Headless ImageJ deconvolution. © 2021 Wiley Periodicals LLC.

Entities:  

Keywords:  Fiji; ImageJ; Jython; Ops; Python; SciJava; deconvolution; image analysis; lifetime analysis; scripting

Mesh:

Year:  2021        PMID: 34370407      PMCID: PMC8363112          DOI: 10.1002/cpz1.204

Source DB:  PubMed          Journal:  Curr Protoc        ISSN: 2691-1299


  21 in total

Review 1.  The ImageJ ecosystem: An open platform for biomedical image analysis.

Authors:  Johannes Schindelin; Curtis T Rueden; Mark C Hiner; Kevin W Eliceiri
Journal:  Mol Reprod Dev       Date:  2015-07-07       Impact factor: 2.609

2.  Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software.

Authors:  Lee Kamentsky; Thouis R Jones; Adam Fraser; Mark-Anthony Bray; David J Logan; Katherine L Madden; Vebjorn Ljosa; Curtis Rueden; Kevin W Eliceiri; Anne E Carpenter
Journal:  Bioinformatics       Date:  2011-02-23       Impact factor: 6.937

3.  ImageJ-MATLAB: a bidirectional framework for scientific image analysis interoperability.

Authors:  Mark C Hiner; Curtis T Rueden; Kevin W Eliceiri
Journal:  Bioinformatics       Date:  2017-02-15       Impact factor: 6.937

4.  ImJoy: an open-source computational platform for the deep learning era.

Authors:  Wei Ouyang; Florian Mueller; Martin Hjelmare; Emma Lundberg; Christophe Zimmer
Journal:  Nat Methods       Date:  2019-12       Impact factor: 28.547

5.  NIH Image to ImageJ: 25 years of image analysis.

Authors:  Caroline A Schneider; Wayne S Rasband; Kevin W Eliceiri
Journal:  Nat Methods       Date:  2012-07       Impact factor: 28.547

6.  OMERO: flexible, model-driven data management for experimental biology.

Authors:  Chris Allan; Jean-Marie Burel; Josh Moore; Colin Blackburn; Melissa Linkert; Scott Loynton; Donald Macdonald; William J Moore; Carlos Neves; Andrew Patterson; Michael Porter; Aleksandra Tarkowska; Brian Loranger; Jerome Avondo; Ingvar Lagerstedt; Luca Lianas; Simone Leo; Katherine Hands; Ron T Hay; Ardan Patwardhan; Christoph Best; Gerard J Kleywegt; Gianluigi Zanetti; Jason R Swedlow
Journal:  Nat Methods       Date:  2012-02-28       Impact factor: 28.547

7.  ImageJ2: ImageJ for the next generation of scientific image data.

Authors:  Curtis T Rueden; Johannes Schindelin; Mark C Hiner; Barry E DeZonia; Alison E Walter; Ellen T Arena; Kevin W Eliceiri
Journal:  BMC Bioinformatics       Date:  2017-11-29       Impact factor: 3.169

8.  Applying phasor approach analysis of multiphoton FLIM measurements to probe the metabolic activity of three-dimensional in vitro cell culture models.

Authors:  Pirmin H Lakner; Michael G Monaghan; Yvonne Möller; Monilola A Olayioye; Katja Schenke-Layland
Journal:  Sci Rep       Date:  2017-02-13       Impact factor: 4.379

Review 9.  SciPy 1.0: fundamental algorithms for scientific computing in Python.

Authors:  Pauli Virtanen; Ralf Gommers; Travis E Oliphant; Matt Haberland; Tyler Reddy; David Cournapeau; Evgeni Burovski; Pearu Peterson; Warren Weckesser; Jonathan Bright; Stéfan J van der Walt; Matthew Brett; Joshua Wilson; K Jarrod Millman; Nikolay Mayorov; Andrew R J Nelson; Eric Jones; Robert Kern; Eric Larson; C J Carey; İlhan Polat; Yu Feng; Eric W Moore; Jake VanderPlas; Denis Laxalde; Josef Perktold; Robert Cimrman; Ian Henriksen; E A Quintero; Charles R Harris; Anne M Archibald; Antônio H Ribeiro; Fabian Pedregosa; Paul van Mulbregt
Journal:  Nat Methods       Date:  2020-02-03       Impact factor: 28.547

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.