A Hitchhiker's guide through the bio-image analysis software universe.
bio-image analysis
open-source
software
Journal
FEBS letters
ISSN: 1873-3468
Titre abrégé: FEBS Lett
Pays: England
ID NLM: 0155157
Informations de publication
Date de publication:
10 2022
10 2022
Historique:
revised:
01
05
2022
received:
01
02
2022
accepted:
12
05
2022
pubmed:
15
7
2022
medline:
13
10
2022
entrez:
14
7
2022
Statut:
ppublish
Résumé
Modern research in the life sciences is unthinkable without computational methods for extracting, quantifying and visualising information derived from microscopy imaging data of biological samples. In the past decade, we observed a dramatic increase in available software packages for these purposes. As it is increasingly difficult to keep track of the number of available image analysis platforms, tool collections, components and emerging technologies, we provide a conservative overview of software that we use in daily routine and give insights into emerging new tools. We give guidance on which aspects to consider when choosing the platform that best suits the user's needs, including aspects such as image data type, skills of the team, infrastructure and community at the institute and availability of time and budget.
Identifiants
pubmed: 35833863
doi: 10.1002/1873-3468.14451
doi:
Types de publication
Journal Article
Review
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2472-2485Informations de copyright
© 2022 The Authors. FEBS Letters published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.
Références
Levet F, Carpenter AE, Eliceiri KW, Kreshuk A, Bankhead P, Haase R. Developing open-source software for bioimage analysis: opportunities and challenges. F1000Res. 2021;10:302.
BioImage Informatics Index. 2022 [cited 2022 Jul 20]. Available from: https://biii.eu
Elixir Community. bio.tools. 2022 [cited 2022 Jul 20]. Available from: https://bio.tools/
Adams D, Lloyd J. The meaning of Liff. London: Pan; 1983.
Bioimage data analysis workflows. Berlin: Springer Nature; 2019.
Lobet G, Draye X, Périlleux C. An online database for plant image analysis software tools. Plant Methods. 2013;9:38.
Hohlbein J, Diederich B, Marsikova B, Reynaud EG, Holden S, Jahr W, et al. Open microscopy in the life sciences: Quo Vadis? arXiv. 2021. https://doi.org/10.48550/arXiv.2110.13951
Gibbs HC, Mota SM, Hart NA, Min SW, Vernino AO, Pritchard AL, et al. Navigating the light-sheet image analysis software landscape: concepts for driving cohesion from data acquisition to analysis. Front Cell Dev Biol. 2021;9:739079.
Software tools for molecular microscopy. 2006 [cited 2022 Jul 20]. Available from: https://en.wikibooks.org/wiki/Software_Tools_For_Molecular_Microscopy
Ollion J, Cochennec J, Loll F, Escudé C, Boudier T. TANGO: a generic tool for high-throughput 3D image analysis for studying nuclear organization. Bioinformatics. 2013;29:1840-1.
Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging. 2012;30:1323-41.
Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage. 2011;54:2033-44.
Pietzsch T, Saalfeld S, Preibisch S, Tomancak P. BigDataViewer: visualization and processing for large image data sets. Nat Methods. 2015;12:481-3.
Hörl D, Rojas Rusak F, Preusser F, Tillberg P, Randel N, Chhetri RK, et al. BigStitcher: reconstructing high-resolution image datasets of cleared and expanded samples. Nat Methods. 2019;16:870-4.
Linkert M, Rueden CT, Allan C, Burel JM, Moore W, Patterson A, et al. Metadata matters: access to image data in the real world. J Cell Biol. 2010;189:777-82.
Blender Foundation. blender.org - home of the Blender project - free and open 3D creation software. 2022 [cited 2022 Jul 20]. Available from: https://www.blender.org/
Domander R, Felder AA, Doube M. BoneJ2 - refactoring established research software. Wellcome Open Res. 2021;6:37.
Saalfeld S, Cardona A, Hartenstein V, Tomancak P. CATMAID: collaborative annotation toolkit for massive amounts of image data. Bioinformatics. 2009;25:1984-6.
Burnley T, Palmer CM, Winn M. Recent developments in the CCP-EM software suite. Acta Crystallogr D Struct Biol. 2017;73:469-77.
CCPi tomographic imaging. 2022 [cited 2022 Jul 20]. Available from: http://www.ccpi.ac.uk/
Stringer C, Wang T, Michaelos M, Pachitariu M. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods. 2021;18:100-6.
McQuin C, Goodman A, Chernyshev V, Kamentsky L, Cimini BA, Karhohs KW, et al. CellProfiler 3.0: next-generation image processing for biology. PLoS Biol. 2018;16:e2005970.
Jones TR, Kang IH, Wheeler DB, Lindquist RA, Papallo A, Sabatini DM, et al. CellProfiler Analyst: data exploration and analysis software for complex image-based screens. BMC Bioinformatics. 2008;9:482.
Sage D, Donati L, Soulez F, Fortun D, Schmit G, Seitz A, et al. DeconvolutionLab2: an open-source software for deconvolution microscopy. Methods. 2017;115:28-41.
Limaye A. Drishti: a volume exploration and presentation tool. In: Stock SR, editor. Developments in X-ray tomography VIII. Bellingham, WA: SPIE; 2012. https://doi.org/10.1117/12.935640
Hu Y, Limaye A, Lu J. Three-dimensional segmentation of computed tomography data using: new tools and developments. R Soc Open Sci. 2020;7:201033.
Klein S, Staring M, Murphy K, Viergever MA, Pluim JPW. elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging. 2010;29:196-205.
Tang G, Peng L, Baldwin PR, Mann DS, Jiang W, Rees I, et al. EMAN2: an extensible image processing suite for electron microscopy. J Struct Biol. 2007;157:38-46.
Müller M, Mönkemöller V, Hennig S, Hübner W, Huser T. Open-source image reconstruction of super-resolution structured illumination microscopy data in ImageJ. Nat Commun. 2016;7:10980.
Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, et al. Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9:676-82.
The Apache Groovy programming language. 2022 [cited 2022 Jul 20]. Available from: https://groovy-lang.org/
Nečas D, Klapetek P. Gwyddion: an open-source software for SPM data analysis. Centr Eur J Phys. 2012;10:181-8.
de Chaumont F, Dallongeville S, Chenouard N, Hervé N, Pop S, Provoost T, et al. Icy: an open bioimage informatics platform for extended reproducible research. Nat Methods. 2012;9:690-6.
Berg S, Kutra D, Kroeger T, Straehle CN, Kausler BX, Haubold C, et al. ilastik: interactive machine learning for (bio)image analysis. Nat Methods. 2019;16:1226-32.
Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9:671-5.
Rueden CT, Schindelin J, Hiner MC, DeZonia BE, Walter AE, Arena ET, et al. ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinformatics. 2017;18:529.
Macro language. 2022 [cited 2022 Jul 20]. Available from: https://imagej.nih.gov/ij/developer/macro/macros.html
Rueden CT, Ackerman J, Arena ET, Eglinger J, Cimini BA, Goodman A, et al. Scientific Community Image Forum: a discussion forum for scientific image software. PLoS Biol. 2019;17:e3000340.
Discourse - civilized discussion. 2022 [cited 2022 Jul 20]. Available from: https://www.discourse.org/
Pietzsch T, Preibisch S, Tomancák P, Saalfeld S. ImgLib2 - generic image processing in Java. Bioinformatics. 2012;28:3009-11.
Kremer JR, Mastronarde DN, McIntosh JR. Computer visualization of three-dimensional image data using IMOD. J Struct Biol. 1996;116:71-6.
Yoo TS, Ackerman MJ, Lorensen WE, Schroeder W, Chalana V, Aylward S, et al. Engineering and algorithm design for an image processing Api: a technical report on ITK-the Insight Toolkit. Stud Health Technol Inform. 2002;85:586-92.
Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage. 2006;31:1116-28.
Stack Overflow Developer Survey 2021. Stack Overflow. 2021 [cited 2022 Jul 20]. Available from: https://insights.stackoverflow.com/survey/2021/?utm_source=social-share&utm_medium=social&utm_campaign=dev-survey-2021
Kluyver T, Ragan-Kelley B, Pérez F, Granger BE, Bussonnier M, Frederic J, et al. Jupyter Notebooks - a publishing format for reproducible computational workflows. In: Loizides F, Schmidt B, editors. Positioning and power in academic publishing: players, agents and agendas. Amsterdam: IOS Press; 2016. p. 87-90.
Berthold MR, Cebron N, Dill F, Gabriel TR, Kötter T, Meinl T, et al. KNIME: the Konstanz information miner. In: Preisach C, Burkhardt H, Schmidt-Thieme L, De R, editors. Data analysis, machine learning and applications. Berlin, Heidelberg: Springer; 2008. p. 319-26.
Helmstaedter M, Briggman KL, Denk W. High-accuracy neurite reconstruction for high-throughput neuroanatomy. Nat Neurosci. 2011;14:1081-8.
Hunter JD. Matplotlib: a 2D graphics environment. Comput Sci Eng. 2007;9:90-5.
Edelstein A, Amodaj N, Hoover K, Vale R, Stuurman N. Computer control of microscopes using μManager. Curr Protoc Mol Biol. 2010;Chapter 14:Unit14.20.
Pinkard H, Stuurman N, Ivanov IE, Anthony NM, Ouyang W, Li B, et al. Pycro-Manager: open-source software for customized and reproducible microscope control. Nat Methods. 2021;18:226-8.
Belevich I, Joensuu M, Kumar D, Vihinen H, Jokitalo E. Microscopy image browser: a platform for segmentation and analysis of multidimensional datasets. PLoS Biol. 2016;14:e1002340.
Barbier de Reuille P, Routier-Kierzkowska AL, Kierzkowski D, Bassel GW, Schüpbach T, Tauriello G, et al. MorphoGraphX: a platform for quantifying morphogenesis in 4D. Elife. 2015;4:05864.
Legland D, Arganda-Carreras I, Andrey P. MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ. Bioinformatics. 2016;32:3532-4.
Laine RF, Tosheva KL, Gustafsson N, Gray RDM, Almada P, Albrecht D, et al. NanoJ: a high-performance open-source super-resolution microscopy toolbox. J Phys D Appl Phys. 2019;52:163001.
Gustafsson N, Culley S, Ashdown G, Owen DM, Pereira PM, Henriques R. Fast live-cell conventional fluorophore nanoscopy with ImageJ through super-resolution radial fluctuations. Nat Commun. 2016;7:12471.
Meijering E, Jacob M, Sarria JCF, Steiner P, Hirling H, Unser M. Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images. Cytometry A. 2004;58:167-76.
Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D, et al. Array programming with NumPy. Nature. 2020;585:357-62.
Python package index download statistics. 2021 [cited 2022 Jul 20]. Available from: https://pypistats.org/top
Ahrens J, Geveci B, Law C. ParaView: an end-user tool for large-data visualization. In: Visualization handbook. Amsterdam: Elsevier; 2005. p. 717-31. https://doi.org/10.1016/B978-012387582-2/50038-1
Püspöki Z, Storath M, Sage D, Unser M. Transforms and operators for directional bioimage analysis: a survey. Adv Anat Embryol Cell Biol. 2016;219:69-93.
Allan C, Burel JM, Moore J, Blackburn C, Linkert M, Loynton S, et al. OMERO: flexible, model-driven data management for experimental biology. Nat Methods. 2012;9:245-53.
Bradski G. The OpenCV library. Dr Dobb's J Software Tools. 2000;120:122-5.
Bankhead P, Loughrey MB, Fernández JA, Dombrowski Y, McArt DG, Dunne PD, et al. QuPath: open source software for digital pathology image analysis. Sci Rep. 2017;7:16878.
The R Community. The R project for statistical computing. 2022 [cited 2022 Jul 20]. Available from: https://www.R-project.org/
Pau G, Fuchs F, Sklyar O, Boutros M, Huber W. EBImage - an R package for image processing with applications to cellular phenotypes. Bioinformatics. 2010;26:979-81.
Scheres SHW. RELION: implementation of a Bayesian approach to cryo-EM structure determination. J Struct Biol. 2012;180:519-30.
SciJava. 2022 [cited 2022 Jul 20]. Available from: https://scijava.org/
van der Walt S, Schönberger JL, Nunez-Iglesias J, Boulogne F, Warner JD, Yager N, et al. scikit-image: image processing in Python. PeerJ. 2014;2:e453.
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825-30.
Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17:261-72.
Mastronarde DN. Automated electron microscope tomography using robust prediction of specimen movements. J Struct Biol. 2005;152:36-51.
Arshadi C, Günther U, Eddison M, Harrington KIS, Ferreira TA. SNT: a unifying toolbox for quantification of neuronal anatomy. Nat Methods. 2021;18:374-7.
Ries J. SMAP: a modular super-resolution microscopy analysis platform for SMLM data. Nat Methods. 2020;17:870-2.
Levet F, Hosy E, Kechkar A, Butler C, Beghin A, Choquet D, et al. SR-Tesseler: a method to segment and quantify localization-based super-resolution microscopy data. Nat Methods. 2015;12:1065-71.
Thévenaz P, Ruttimann UE, Unser M. A pyramid approach to subpixel registration based on intensity. IEEE Trans Image Process. 1998;7:27-41.
Schmidt U, Weigert M, Broaddus C, Myers G. Cell detection with star-convex polygons. In: Frangi AF, editor. Medical image computing and computer assisted intervention - MICCAI 2018. Cham: Springer International Publishing; 2018. p. 265-73.
Weigert M, Schmidt U, Haase R, Sugawara K, Myers G. Star-convex polyhedra for 3D object detection and segmentation in microscopy. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE; 2020. https://doi.org/10.1109/wacv45572.2020.9093435
Ovesný M, Křížek P, Borkovec J, Svindrych Z, Hagen GM. ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging. Bioinformatics. 2014;30:2389-90.
Gürsoy D, De Carlo F, Xiao X, Jacobsen C. TomoPy: a framework for the analysis of synchrotron tomographic data. J Synchrotron Radiat. 2014;21:1188-93.
Levin BDA, Jiang Y, Padgett E, Waldon S, Quammen C, Harris C, et al. Tutorial on the visualization of volumetric data using tomviz. Micros Today. 2018;26:12-7.
Tinevez J-Y, Perry N, Schindelin J, Hoopes GM, Reynolds GD, Laplantine E, et al. TrackMate: an open and extensible platform for single-particle tracking. Methods. 2017;115:80-90.
Arganda-Carreras I, Kaynig V, Rueden C, Eliceiri KW, Schindelin J, Cardona A, et al. Trainable Weka segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics. 2017;33:2424-6.
Witten IH, Frank E, Hall MA, Pal CJ. Data mining: practical machine learning tools and techniques. Burlington, WA: Morgan Kaufmann; 2016.
Cardona A, Saalfeld S, Schindelin J, Arganda-Carreras I, Preibisch S, Longair M, et al. TrakEM2 software for neural circuit reconstruction. PLoS One. 2012;7:e38011.
Weigert M, Schmidt U, Boothe T, Müller A, Dibrov A, Jain A, et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat Methods. 2018;15:1090-7.
Krull A, Buchholz T-O, Jug F. Noise2Void - learning denoising from single noisy images. arXiv. 2018. https://doi.org/10.48550/arXiv.1811.10980
Batson J, Royer L. Noise2Self: blind denoising by self-supervision. 2019.
Aigouy B, Cortes C, Liu S, Prud'Homme B. EPySeg: a coding-free solution for automated segmentation of epithelia using deep learning. Development. 2020;147:dev194589.
Mathis A, Mamidanna P, Cury KM, Abe T, Murthy VN, Mathis MW, et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat Neurosci. 2018;21:1281-9.
Belthangady C, Royer LA. Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction. Nat Methods. 2019;16:1215-25.
BioImageIO. BioImage Model Zoo. 2022 [cited 2022 Jul 20]. Available from: https://bioimage.io
Tensorflow-Developers. TensorFlow. Zenodo. 2021. https://doi.org/10.5281/ZENODO.4724125
Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, et al. PyTorch: an imperative style, high-performance deep learning library. arXiv. 2019; arXiv:1912.01703.
Gómez-de-Mariscal E, García-López-de-Haro C, Ouyang W, Donati L, Lundberg E, Unser M, et al. DeepImageJ: a user-friendly environment to run deep learning models in ImageJ. Nat Methods. 2021;18:1192-5.
von Chamier L, Laine RF, Jukkala J, Spahn C, Krentzel D, Nehme E, et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun. 2021;12:2276.
Ouyang W, Mueller F, Hjelmare M, Lundberg E, Zimmer C. ImJoy: an open-source computational platform for the deep learning era. Nat Methods. 2019;16:1199-200.
Belevich I, Jokitalo E. DeepMIB: user-friendly and open-source software for training of deep learning network for biological image segmentation. PLoS Comput Biol. 2021;17:e1008374.
Sofroniew N, Lambert T, Evans K, Nunez-Iglesias J, Bokota G, Peña-Castellanos G, et al. napari/napari: 0.4.12rc2. Zenodo. 2021. https://doi.org/10.5281/ZENODO.3555620
Perkel JM. Python power-up: new image tool visualizes complex data. Nature. 2021;600:347-8.
Vergara HM, Pape C, Meechan KI, Zinchenko V, Genoud C, Wanner AA, et al. Whole-body integration of gene expression and single-cell morphology. Cell. 2021;184:4819-4837.e22.
Chiaruttini N, Burri O, Haub P, Guiet H, Sordet-Dessimoz J, Seitz A. An open-source whole slide image registration workflow at cellular precision using Fiji, QuPath and elastix. Front Comput Sci. 2022;3. https://doi.org/10.3389/fcomp.2021.780026
Schmid B, Tripal P, Fraaß T, Kersten C, Ruder B, Grüneboom A, et al. 3Dscript: animating 3D/4D microscopy data using a natural-language-based syntax. Nat Methods. 2019;16:278-80.
Afgan E, Baker D, Batut B, van den Beek M, Bouvier D, Čech M, et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 2018;46:W537-44.
Marée R, Rollus L, Stévens B, Hoyoux R, Louppe G, Vandaele R, et al. Collaborative analysis of multi-gigapixel imaging data using Cytomine. Bioinformatics. 2016;32:1395-401.
Rubens U, Mormont R, Paavolainen L, Bäcker V, Pavie B, Scholz LA, et al. BIAFLOWS: a collaborative framework to reproducibly deploy and benchmark bioimage analysis workflows. Patterns (N Y). 2020;1:100040.
Tischer C, Ravindran A, Reither S, Chiaruttini N, Pepperkok R, Norlin N. BigDataProcessor2: a free and open-source Fiji plugin for inspection and processing of TB sized image data. Bioinformatics. 2021;37(18):3079-81. https://doi.org/10.1093/bioinformatics/btab106
Rubens U, Hoyoux R, Vanosmael L, Ouras M, Tasset M, Hamilton C, et al. Cytomine: toward an open and collaborative software platform for digital pathology bridged to molecular investigations. Proteomics Clin Appl. 2019;13:e1800057.
Boergens KM, Berning M, Bocklisch T, Bräunlein D, Drawitsch F, Frohnhofen J, et al. webKnossos: efficient online 3D data annotation for connectomics. Nat Methods. 2017;14:691-4.
Rocklin M. Dask: parallel computation with blocked algorithms and task scheduling. In: Proceedings of the 14th Python in Science Conference. SciPy; 2015. https://doi.org/10.25080/majora-7b98e3ed-013
Okuta R, Unno Y, Nishino D, Hido S, Loomis C. CuPy: a NumPy-compatible library for NVIDIA GPU calculations. In: Proceedings of workshop on machine learning systems (LearningSys) in the thirty-first annual conference on neural information processing systems (NIPS); 2017 [cited 2022 Jul 20]. Available from: http://learningsys.org/nips17/assets/papers/paper_16.pdf
Haase R, Royer LA, Steinbach P, Schmidt D, Dibrov A, Schmidt U, et al. CLIJ: GPU-accelerated image processing for everyone. Nat. Methods. 2020;17:5-6.
Malyshau D, Ninomiya K, Jones B, editors. WebGPU. 2022 [cited 2022 Jul 20]. Available from: https://www.w3.org/TR/webgpu/
Haase R, Jain A, Rigaud S, Vorkel D, Rajasekhar P, Suckert T, et al. Interactive design of GPU-accelerated image data flow graphs and cross-platform deployment using multi-lingual code generation. bioRxiv. 2020. https://doi.org/10.1101/2020.11.19.386565
Stoltzfus CR, Filipek J, Gern BH, Olin BE, Leal JM, Wu Y, et al. CytoMAP: a spatial analysis toolbox reveals features of myeloid cell organization in lymphoid tissues. Cell Rep. 2020;31:107523.
Palla G, Spitzer H, Klein M, Fischer D, Schaar AC, Kuemmerle LB, et al. Squidpy: a scalable framework for spatial single cell analysis. bioRxiv. 2021. https://doi.org/10.1101/2021.02.19.431994
Axelrod S, Cai M, Carr AJ, Freeman J, Ganguli D, Kiggins JT, et al. starfish: scalable pipelines for image-criptomics. J Open Source Softw. 2021;6:2440.
Haase R. Image processing filters for grids of cells analogous to filters processing grids of pixels. Front Comput Sci. 2021;3. https://doi.org/10.3389/fcomp.2021.774396
Solorzano L, Partel G, Wählby C. TissUUmaps: interactive visualization of large-scale spatial gene expression and tissue morphology data. Bioinformatics. 2020;36:4363-5.
Pielawski N, Andersson A, Avenel C, Behanova A, Chelebian E, Klemm A, et al. TissUUmaps 3: interactive visualization and quality assessment of large-scale spatial omics data. bioRxiv. 2022. https://doi.org/10.1101/2022.01.28.478131
Sage D, Pham TA, Babcock H, Lukes T, Pengo T, Chao J, et al. Super-resolution fight club: assessment of 2D and 3D single-molecule localization microscopy software. Nat Methods. 2019;16:387-95.
Culley S, Albrecht D, Jacobs C, Pereira PM, Leterrier C, Mercer J, et al. Quantitative mapping and minimization of super-resolution optical imaging artifacts. Nat Methods. 2018;15:263-6.
Ball G, Demmerle J, Kaufmann R, Davis I, Dobbie IM, Schermelleh L. SIMcheck: a toolbox for successful super-resolution structured illumination microscopy. Sci Rep. 2015;5:15915.
Marsh RJ, Costello I, Gorey M-A, Ma D, Huang F, Gautel M, et al. Sub-diffraction error mapping for localisation microscopy images. Nat Commun. 2021;12:5611.
Miles A, Kirkham J, Durant M, Bourbeau J, Onalan T, Hamman J, et al. zarr-developers/zarr-python: v2.4.0. 2020. https://doi.org/10.5281/zenodo.3773450
Moore J, Allan C, Besson S, Burel J-M, Diel E, Gault D, et al. OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies. Nat Methods. 2021;18:1496-8.
Saalfeld S, Pisarev I, Hanslovsky P, Bogovic J, Champion A, Rueden C, et al. N5: Not HDF5. 2017 [cited 2022 Jul 20]. Available from: https://github.com/saalfeldlab/n5
Miura K, Nørrelykke SF. Reproducible image handling and analysis. EMBO J. 2021;40:e105889.
Ulman V, Maška M, KEG M, Ronneberger O, Haubold C, Harder N, et al. An objective comparison of cell-tracking algorithms. Nat Methods. 2017;14:1141-52.
Cell Tracking Benchmark. 2022 [cited 2022 Jul 20]. Available from: http://celltrackingchallenge.net/latest-ctb-results/
Arganda-Carreras I, Turaga SC, Berger DR, Cireşan D, Giusti A, Gambardella LM, et al. Crowdsourcing the creation of image segmentation algorithms for connectomics. Front Neuroanat. 2015;9:142.
Wei D, Lin Z, Franco-Barranco D, Wendt N, Liu X, Yin W, et al. MitoEM Dataset: large-scale 3D mitochondria instance segmentation from EM images. Med Image Comput Comput Assist Interv. 2020;12265:66-76.
NEUBIAS. NEUBIAS Academy youtube channel. 2020 [cited 2022 Jul 20]. Available from: https://www.youtube.com/neubias