Literature DB >> 33785609

A guide to accurate reporting in digital image processing - can anyone reproduce your quantitative analysis?

Jesse Aaron1, Teng-Leong Chew2.   

Abstract

Considerable attention has been recently paid to improving replicability and reproducibility in life science research. This has resulted in commendable efforts to standardize a variety of reagents, assays, cell lines and other resources. However, given that microscopy is a dominant tool for biologists, comparatively little discussion has been offered regarding how the proper reporting and documentation of microscopy relevant details should be handled. Image processing is a critical step of almost any microscopy-based experiment; however, improper, or incomplete reporting of its use in the literature is pervasive. The chosen details of an image processing workflow can dramatically determine the outcome of subsequent analyses, and indeed, the overall conclusions of a study. This Review aims to illustrate how proper reporting of image processing methodology improves scientific reproducibility and strengthens the biological conclusions derived from the results.
© 2021. Published by The Company of Biologists Ltd.

Keywords:  Accurate reporting; Data reproducibility; Image analysis; Image processing; Microscopy

Mesh:

Year:  2021        PMID: 33785609     DOI: 10.1242/jcs.254151

Source DB:  PubMed          Journal:  J Cell Sci        ISSN: 0021-9533            Impact factor:   5.285


  7 in total

1.  Glutamine-dependent signaling controls pluripotent stem cell fate.

Authors:  Vivian Lu; Irena J Roy; Alejandro Torres; James H Joly; Fasih M Ahsan; Nicholas A Graham; Michael A Teitell
Journal:  Dev Cell       Date:  2022-02-24       Impact factor: 12.270

2.  Quantifying Human Natural Killer Cell Migration by Imaging and Image Analysis.

Authors:  Amera L Martinez; Michael J Shannon; Shira E Eisman; Everardo Hegewisch-Solloa; Aneeza N Asif; Tasneem A M Ebrahim; Emily M Mace
Journal:  Methods Mol Biol       Date:  2022

3.  MethodsJ2: a software tool to capture metadata and generate comprehensive microscopy methods text.

Authors:  Joel Ryan; Thomas Pengo; Alex Rigano; Paula Montero Llopis; Michelle S Itano; Lisa A Cameron; Guillermo Marqués; Caterina Strambio-De-Castillia; Mark A Sanders; Claire M Brown
Journal:  Nat Methods       Date:  2021-12       Impact factor: 47.990

4.  Towards community-driven metadata standards for light microscopy: tiered specifications extending the OME model.

Authors:  Mathias Hammer; Maximiliaan Huisman; Alessandro Rigano; Ulrike Boehm; James J Chambers; Nathalie Gaudreault; Alison J North; Jaime A Pimentel; Damir Sudar; Peter Bajcsy; Claire M Brown; Alexander D Corbett; Orestis Faklaris; Judith Lacoste; Alex Laude; Glyn Nelson; Roland Nitschke; Farzin Farzam; Carlas S Smith; David Grunwald; Caterina Strambio-De-Castillia
Journal:  Nat Methods       Date:  2021-12       Impact factor: 47.990

5.  Avoiding a replication crisis in deep-learning-based bioimage analysis.

Authors:  Romain F Laine; Ignacio Arganda-Carreras; Ricardo Henriques; Guillaume Jacquemet
Journal:  Nat Methods       Date:  2021-10       Impact factor: 28.547

6.  Celebrating FocalPlane and microscopy in Disease Models & Mechanisms.

Authors:  Julija Hmeljak; Esperanza Agullo-Pascual
Journal:  Dis Model Mech       Date:  2021-07-19       Impact factor: 5.758

7.  A Systematic, Open-Science Framework for Quantification of Cell-Types in Mouse Brain Sections Using Fluorescence Microscopy.

Authors:  Juan C Sanchez-Arias; Micaël Carrier; Simona D Frederiksen; Olga Shevtsova; Chloe McKee; Emma van der Slagt; Elisa Gonçalves de Andrade; Hai Lam Nguyen; Penelope A Young; Marie-Ève Tremblay; Leigh Anne Swayne
Journal:  Front Neuroanat       Date:  2021-12-06       Impact factor: 3.856

  7 in total

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