BioImageIT: A novel python-based architecture for reproducible bio-image workflows.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	BioImageIT: A novel python-based architecture for reproducible bio-image workflows.
Συγγραφείς:	Masson A; Inria Center at University of Rennes, Rennes, France.; SAIRPICO Team, Paris, France.; U1339 INSERM, Institut Curie, Chemical Biology of Cancer Unit, Paris, France., Prigent S; Inria Center at University of Rennes, Rennes, France., Valades-Cruz CA; Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China., Leconte L; Institute of Human Genetics, CNRS UMR9002, National Infrastructure France-BioImaging, Montpellier, France., Maury L; Inria Center at University of Rennes, Rennes, France.; SAIRPICO Team, Paris, France.; U1339 INSERM, Institut Curie, Chemical Biology of Cancer Unit, Paris, France., Salamero J; Institut Pasteur de Montevideo, Montevideo, Uruguay., Kervrann C; Inria Center at University of Rennes, Rennes, France.; SAIRPICO Team, Paris, France.; U1339 INSERM, Institut Curie, Chemical Biology of Cancer Unit, Paris, France.
Πηγή:	Journal of microscopy [J Microsc] 2026 Jul 06. Date of Electronic Publication: 2026 Jul 06.
Publication Model:	Ahead of Print
Τύπος έκδοσης:	Journal Article
Γλώσσα:	English
Στοιχεία περιοδικού:	Publisher: Published for the Royal Microscopical Society by Blackwell Scientific Publications Country of Publication: England NLM ID: 0204522 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1365-2818 (Electronic) Linking ISSN: 00222720 NLM ISO Abbreviation: J Microsc Subsets: MEDLINE
Imprint Name(s):	Original Publication: Oxford, Published for the Royal Microscopical Society by Blackwell Scientific Publications.
Περίληψη:	Recent advances in light microscopy have transformed the scale and complexity of biological imaging data, creating an urgent need for sophisticated computational pipelines capable of extracting meaningful biological insights. However, the current software ecosystem for bio-image analysis remains highly fragmented, with researchers needing to integrate tools written in disparate programming languages and architectural paradigms. This fragmentation gives rise to 'dependency hell' and creates significant technical barriers for life scientists. We present the novel and flexible architecture of BioImageIT, a lightweight open-source workflow management system designed to bridge the gap between advanced computational tools and end-user bio-image analysts. Built upon Python, BioImageIT has evolved into a dual interface architecture: a node-based visual programming GUI alongside a comprehensive Python Application Programming Interface (API). The system features the Wetlands environment management system for automatic dependency resolution, and adopts pandas DataFrames as the universal data structure for inter-node communication. BioImageIT enforces adherence to FAIR principles (Findable, Accessible, Interoperable, Reusable) throughout the analysis lifecycle, automatically capturing comprehensive metadata for every processing step. The architecture abstracts the underlying computational infrastructure, laying the groundwork for seamless scaling from local workstations to high-performance computing (HPC) clusters-a capability currently under active development. (© 2026 Royal Microscopical Society.)
References:	Eliceiri, K. W., Berthold, M. R., Goldberg, I. G., Ibáñez, L., Manjunath, B. S., Martone, M. E., Murphy, R. F., Peng, H., Plant, A. L., Roysam, B., Stuurman, N., Swedlow, J. R., Tomancak, P., & Carpenter, A. E. (2012). Biological imaging software tools. Nature Methods, 9(7), 697–710. https://doi.org/10.1038/nmeth.2084. Shariff, A., Kangas, J., Coelho, L. P., Quinn, S., & Murphy, R. F. (2010). Automated image analysis for high‐content screening and analysis. Journal of Biomolecular Screening, 15(7), 726–734. https://doi.org/10.1177/1087057110370894. Schermelleh, L., Ferrand, A., Huser, T., Eggeling, C., Sauer, M., Biehlmaier, O., & Drummen, G. P. C. (2019). Super‐resolution microscopy demystified. Nature Cell Biology, 21(1), 72–84. https://doi.org/10.1038/s41556‐018‐0251‐8. Meijering, E., Carpenter, A. E., Peng, H., Hamprecht, F. A., & Olivo‐Marin, J.‐C. (2016). Imagining the future of bioimage analysis. Nature Biotechnology, 34(12), 1250–1255. https://doi.org/10.1038/nbt.3722. Schindelin, J., Arganda‐Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., Tinevez, J.‐Y., White, D. J., Hartenstein, V., Eliceiri, K., Tomancak, P., & Cardona, A. (2012). Fiji: An open‐source platform for biological‐image analysis. Nature Methods, 9(7), 676–682. https://doi.org/10.1038/nmeth.2019. Boettiger, C. (2015). An introduction to docker for reproducible research. ACM SIGOPS Operating Systems Review, 49(1), 71–79. https://doi.org/10.1093/nar/gky379,. Prigent, S., Valades‐Cruz, C. A., Leconte, L., Maury, L., Salamero, J., & Kervrann, C. (2022). Bioimageit: Open‐source framework for integration of image data management with analysis. Nature Methods, 19(11), 1328–1330. https://doi.org/10.1093/bib/bbw020,. Carpenter, A. E., Jones, T. R., Lamprecht, M. R., Clarke, C., Kang, I. H., Friman, O., Guertin, D. A., Chang, J. H., Lindquist, R. A., Moffat, J., Golland, P., & Sabatini, D. M. (2006). Cellprofiler: Image analysis software for identifying and quantifying cell phenotypes. Genome Biology, 7(10), R100. https://doi.org/10.1186/gb‐2006‐7‐10‐r100. Stirling, D. R., Swain‐Bowden, M. J., Lucas, A. M., Carpenter, A. E., Cimini, B. A., & Goodman, A. (2021). Cellprofiler 4: Improvements in speed, utility and usability. BMC Bioinformatics, 22(1), 433. https://doi.org/10.1186/s12859‐021‐04344‐9. Cross, S. J., Fisher, J. D., & Jepson, M. A. (2024). ModularImageAnalysis (MIA): Assembly of modularised image and object analysis workflows in ImageJ. Journal of Microscopy, 296(3), 173–183. https://doi.org/10.1111/jmi.13227. Gerst, R., Cseresnyés, Z., & Figge, M. T. (2023). JIPipe: Visual batch processing for ImageJ. Nature Methods, 20(2), 168–169. https://doi.org/10.1038/s41592‐022‐01744‐4. Berthold, M. R., Cebron, N., Dill, F., Gabriel, T. R., Kötter, T., Meinl, T., Ohl, P., Sieb, C., Thiel, K., & Wiswedel, B. (2008). Knime: The konstanz information miner. In Preisach, C., Burkhardt, H., Schmidt‐Thieme, L., & Decker, R. (Eds.), Data analysis, machine learning and applications (pp. 319–326). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.25080/Majora‐92bf1922‐00a. de Chaumont, F., Dallongeville, S., Chenouard, N., Hervé, N., Pop, S., Provoost, T., Meas‐Yedid, V., Pankajakshan, P., Lecomte, T., Le Montagner, Y., Lagache, T., Dufour, A., & Olivo‐Marin, J.‐C. (2012). Icy: An open bioimage informatics platform for extended reproducible research. Nature Methods, 9(7), 690–696. https://doi.org/10.1038/nmeth.2075. Bankhead, P., Loughrey, M. B., Fernández, J. A., Dombrowski, Y., McArt, D. G., Dunne, P. D., McQuaid, S., Gray, R. T., Murray, L. J., Coleman, H. G., James, J. A., Salto‐Tellez, M., & Hamilton, P. W. (2017). QuPath: Open source software for digital pathology image analysis. Scientific Reports, 7(1), 16878. https://doi.org/10.1038/s41598‐017‐17204‐5. Haase, R., & Savill, R. (2024). napari‐workflows: Data structures for managing image processing workflows in napari. Python Package Index. Software package, version 0.2.11. https://pypi.org/project/napari‐workflows/. Luik, T. T., Rosas‐Bertolini, R., Reits, E. A. J., Hoebe, R. A., & Krawczyk, P. M. (2024). BIOMERO: A scalable and extensible image analysis framework. Patterns, 5(8), 101024. https://doi.org/10.1016/j.patter.2024.101024. Afgan, E., Baker, D., Batut, B., van den Beek, M., Bouvier, D., Čech, M., Chilton, J., Clements, D., Coraor, N., Grüning, B. A., Guerler, A., Hillman‐Jackson, J., Hiltemann, S., Jalili, V., Rasche, H., Soranzo, N., Goecks, J., Taylor, J., Nekrutenko, A., & Blankenberg, D. (2018). The galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Research, 46(W1), W537–W544. https://doi.org/10.1093/nar/gky379. Mölder, F., Jablonski, K. P., Letcher, B., Hall, M. B., van Dyken, P. C., Tomkins‐Tinch, C. H., Sochat, V., Forster, J., Vieira, F. G., Meesters, C., Lee, S., Twardziok, S. O., Kanitz, A., VanCampen, J., Malladi, V., Wilm, A., Holtgrewe, M., Rahmann, S., Nahnsen, S., & Köster, J. (2021). Sustainable data analysis with Snakemake. F1000Research, 10, 33. https://doi.org/10.12688/f1000research.29032.2. Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316–319. https://doi.org/10.1038/nbt.3820. Sofroniew, N., Lambert, T., Bokota, G., Nunez‐Iglesias, J., Sobolewski, P., Sweet, A., Gaifas, L., Evans, K., Burt, A., Doncila Pop, D., Yamauchi, K., Weber Mendonça, M., Rodríguez‐Guerra, J., Liu, L., Buckley, G., Vierdag, W.‐M., Anderson, A., Monko, T., Willing, C., … Witz, G. (2026). napari: A multi‐dimensional image viewer for python. Zenodo. https://doi.org/10.5281/zenodo.18374344. Appose Developers. (2026). Appose: Interprocess cooperation with shared memory. Appose Project. Software package, version 0.11.0. https://apposed.org/. Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.‐W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3(1), 160018. https://doi.org/10.1038/sdata.2016.18. McKinney, W. (2010). Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference. Austin. https://doi.org/10.25080/Majora‐92bf1922‐00a. Lunin, I., & Cabrera, P. (2024). PyFlow: Visual scripting framework for Python. GitHub repository. Version 3.0.0. https://github.com/pedroCabrera/PyFlow. Fischer, T., Vollprecht, W., Zalmstra, B., Arts, R., de Jager, T., Fontan, A., Hines, A. D., Milford, M., Traversaro, S., Claes, D., & Raine, S. (2025). Pixi: Unified software development and distribution for robotics and AI. arXiv:2511.04827. https://arxiv.org/abs/2511.04827. Boulanger, J., Kervrann, C., Bouthemy, P., Elbau, P., Sibarita, J.‐B., & Salamero, J. (2010). Patch‐based nonlocal functional for denoising fluorescence microscopy image sequences. IEEE Transactions on Medical Imaging, 29(2), 442–454. https://doi.org/10.1038/nmeth.2084. Basset, A., Boulanger, J., Salamero, J., Bouthemy, P., & Kervrann, C. (2015). Adaptive spot detection with optimal scale selection in fluorescence microscopy images. IEEE Transactions on Image Processing, 24(11), 4512–4527. https://doi.org/10.1093/nar/gky379. Ershov, D., Phan, M.‐S., Pylvänäinen, J. W., Rigaud, S. U., Le Blanc, L., Charles‐Orszag, A., Conway, J. R. W., Laine, R. F., Roy, N. H., Bonazzi, D., Duménil, G., Jacquemet, G., & Tinevez, J.‐Y. (2022). TrackMate 7: Integrating state‐of‐the‐art segmentation algorithms into tracking pipelines. Nature Methods, 19(7), 829–832. https://doi.org/10.1038/s41592‐022‐01507‐1. Schmidt, U., Weigert, M., Broaddus, C., & Myers, G. (2018). Cell detection with star‐convex polygons. In Frangi, A. F., Schnabel, J. A., Davatzikos, C., Alberola‐López, C., & Fichtinger, G. (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (pp. 265–273). Springer International Publishing. https://doi.org/10.1038/nmeth.2084. Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021). Cellpose: A generalist algorithm for cellular segmentation. Nature Methods, 18(1), 100–106. https://doi.org/10.1038/s41592‐020‐01018‐x. Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.‐Y., Dollár, P., & Girshick, R. (2023). Segment anything. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 3992–4003). IEEE. https://doi.org/10.1038/nmeth.2084. Allan, C., Burel, J.‐M., Moore, J., Blackburn, C., Linkert, M., Loynton, S., MacDonald, D., Moore, W. J., Neves, C., Patterson, A., Porter, M., Tarkowska, A., Loranger, B., Avondo, J., Lagerstedt, I., Lianas, L., Leo, S., Hands, K., Hay, R. T., … Swedlow, J. R. (2012). OMERO: Flexible, model‐driven data management for experimental biology. Nature Methods, 9(3), 245–253. https://doi.org/10.1038/nmeth.1896. Amstutz, P., Crusoe, M. R., Tijanić, N., Chapman, B., Chilton, J., Heuer, M., Kartashov, A., Leehr, D., Ménager, H., Nedeljkovich, M., Scales, M., Soiland‐Reyes, S., & Stojanovic, L. (2016). Common workflow language, v1.0. UC Davis. https://doi.org/10.6084/m9.figshare.3115156.v2. Babuji, Y., Woodard, A., Li, Z., Katz, D. S., Clifford, B., Kumar, R., Lacinski, L., Chard, R., Wozniak, J. M., Foster, I., Wilde, M., & Chard, K. (2019). Parsl: Pervasive parallel programming in python. In Proceedings of the 28th International Symposium on High‐Performance Parallel and Distributed Computing, HPDC '19 (pp. 25–36). Association for Computing Machinery. https://doi.org/10.1145/3307681.3325400. Hategan‐Marandiuc, M., Merzky, A., Collier, N., Maheshwari, K., Ozik, J., Turilli, M., Wilke, A., Wozniak, J. M., Chard, K., Foster, I., da Silva, R. F., Jha, S., & Laney, D. (2023). Psi/j: A portable interface for submitting, monitoring, and managing jobs. In 2023 IEEE 19th International Conference on e‐Science (e‐Science) (pp. 1–10). IEEE. https://doi.org/10.1038/nmeth.2084. Colvin, S., Jolibois, E., Ramezani, H., Garcia Badaracco, A., Dorsey, T., Montague, D., Matveenko, S., Trylesinski, M., Runkle, S., Hewitt, D., Hall, A., & Plot, V. (2025). Pydantic validation. https://docs.pydantic.dev/latest/. Bantilan, N. (2020). pandera: Statistical data validation of pandas dataframes. In Proceedings of the 19th Python in Science Conference (SCIPY 2020). https://doi.org/10.25080/Majora‐342d178e‐010.
Grant Information:	Inria Center at University of Rennes; Institut Curie; ANR-10-INBS-04-07 French National Research Agency; France BioImaging Infrastructure
Contributed Indexing:	Keywords: FAIR principles; Python; bio‐image analysis; image processing; microscopy; reproducibility; visual programming; workflow management
Entry Date(s):	Date Created: 20260706 Latest Revision: 20260706
Update Code:	20260706
DOI:	10.1111/jmi.70141
PMID:	42405690
Βάση Δεδομένων:	MEDLINE

Full Text Finder

Περιγραφή
ISSN:	1365-2818
DOI:	10.1111/jmi.70141