Comparison of Independent Analyses of Identical Image Sets Reveals Significant Analyst-to-Analyst Variability
Jazyk angličtina Země Spojené státy americké Médium electronic-ecollection
Typ dokumentu časopisecké články, srovnávací studie
PubMed
41111557
PubMed Central
PMC12530733
DOI
10.7171/3fc1f5fe.60f1999c
PII: 3fc1f5fe.60f1999c
Knihovny.cz E-zdroje
- Klíčová slova
- bioimage analysis, image analysis, image analysis challenge, image processing, inter-analyst variation, reliability, reproducibility, variability,
- MeSH
- algoritmy MeSH
- lidé MeSH
- mikroskopie * metody MeSH
- počítačové zpracování obrazu * metody MeSH
- poměr signál - šum MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- srovnávací studie MeSH
The Light Microscopy Research Group, a research group with the Association of Biomolecular Resource Facilities, organized a global study where participants were given artificially generated images of various specimens with various signal-to-noise and object proximity levels. Users were tasked with segmenting the images and providing measured metrics as part of the study. Rather than ranking algorithms, our goal was to study the sources of variability of the results across participants given the same task. This study highlights that substantial variability can exist between independent analyses of identical datasets, even when the analysis problem is relatively straightforward and the analysts are experienced. These findings further support the need for data and analysis methods that follow the findable, accessible, interoperable, reusable (FAIR) principles.
Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czechia
Dept of Biomedical Sciences School of Public Health State University of New York
https abrf org research groups imaging flow light microscopy
James Madison University Department of Biology Light Microscopy and Imaging Facility HarrisonburgVA
Minnesota Supercomputing Institute University of Minnesota Twin Cities MinneapolisMN
New York State Department of Health Wadsworth Center AlbanyNY
Zobrazit více v PubMed
Mische SM, Fisher NC, Meyn SM, et al. A review of the scientific rigor, reproducibility, and transparency studies conducted by the ABRF research groups. PubMed DOI PMC
Botvinik-Nezer R, Holzmeister F, Camerer CF, et al. Variability in the analysis of a single neuroimaging dataset by many teams. PubMed DOI PMC
Culley S, Caballero AC, Burden JJ, Uhlmann V. Made to measure: an introduction to quantifying microscopy data in the life sciences. PubMed DOI
Aaron J, Chew TL. A guide to accurate reporting in digital image processing – can anyone reproduce your quantitative analysis? PubMed DOI
Schmied C, Jambor HK. Effective image visualization for publications - a workflow using open access tools and concepts. PubMed DOI PMC
Marques O. Core image. In: SpringerBriefs in Computer Science: Springer, Cham; 2020:19-27. 10.1007/978-3-030-54032-6_3 DOI
Wiesner D, Svoboda D, Maška M, Kozubek M. CytoPacq: a web-interface for simulating multi-dimensional cell imaging. PubMed DOI PMC
Maška M, Ulman V, Delgado-Rodriguez P, et al. The cell tracking challenge: 10 years of objective benchmarking. PubMed DOI PMC
Holz D, Ichim AE, Tombari F, Rusu RB, Behnke S. Registration with the point cloud library: a modular framework for aligning in 3-D. In: IEEE Robotics & Automation Magazine. IEEE; 2015;22(4):110-24. 10.1109/MRA.2015.2432331 DOI
Gatti AA, Khallaghi S. PyCPD: Pure NumPy implementation of the coherent point drift algorithm. DOI
Virtanen P, Gommers R, Oliphant TE, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. PubMed DOI PMC
Lambert T, Waters J. Towards effective adoption of novel image analysis methods. PubMed DOI