{"entity": "researcher", "timestamp": "2026-08-20T20:37:36.936Z", "family": "Sage", "given": "Daniel", "initials": "D", "orcid": "0000-0002-1150-1623", "affiliations": ["Biomedical Imaging Group and Center for Imaging, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/919b0180005444d88757db8e63b80d83.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/919b0180005444d88757db8e63b80d83"}}, "publications": [{"entity": "publication", "iuid": "1840a8a90ea843e8874a0033568988d2", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/1840a8a90ea843e8874a0033568988d2.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/1840a8a90ea843e8874a0033568988d2"}}, "title": "Bridging the gap: Integrating cutting-edge techniques into biological imaging with deepImageJ.", "authors": [{"family": "Fuster-Barcel\u00f3", "given": "Caterina", "initials": "C", "orcid": "0000-0002-4784-6957", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/881d36fa227445c9b381a03990b5ed49.json"}}, {"family": "Garc\u00eda-L\u00f3pez-de-Haro", "given": "Carlos", "initials": "C"}, {"family": "G\u00f3mez-de-Mariscal", "given": "Estibaliz", "initials": "E"}, {"family": "Ouyang", "given": "Wei", "initials": "W"}, {"family": "Olivo-Marin", "given": "Jean-Christophe", "initials": "JC", "orcid": "0000-0001-6796-0696", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f2558d56edd54d0e87904930c8ec54f6.json"}}, {"family": "Sage", "given": "Daniel", "initials": "D", "orcid": "0000-0002-1150-1623", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/919b0180005444d88757db8e63b80d83.json"}}, {"family": "Mu\u00f1oz-Barrutia", "given": "Arrate", "initials": "A", "orcid": "0000-0002-1573-1661", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1f71dcceb0284105a7033f2ed195b9c1.json"}}], "type": "journal article", "published": "2024-11-22", "journal": {"title": "Biol Imaging", "issn": "2633-903X", "volume": "4", "pages": "e14", "issn-l": null}, "abstract": "This manuscript showcases the latest advancements in deepImageJ, a pivotal Fiji/ImageJ plugin for bioimage analysis in life sciences. The plugin, known for its user-friendly interface, facilitates the application of diverse pre-trained convolutional neural networks to custom data. The manuscript demonstrates several deepImageJ capabilities, particularly in deploying complex pipelines, three-dimensional (3D) image analysis, and processing large images. A key development is the integration of the Java Deep Learning Library, expanding deepImageJ's compatibility with various deep learning (DL) frameworks, including TensorFlow, PyTorch, and ONNX. This allows for running multiple engines within a single Fiji/ImageJ instance, streamlining complex bioimage analysis workflows. The manuscript details three case studies to demonstrate these capabilities. The first case study explores integrated image-to-image translation followed by nuclei segmentation. The second case study focuses on 3D nuclei segmentation. The third case study showcases large image volume segmentation and compatibility with the BioImage Model Zoo. These use cases underscore deepImageJ's versatility and power to make advanced DLmore accessible and efficient for bioimage analysis. The new developments within deepImageJ seek to provide a more flexible and enriched user-friendly framework to enable next-generation image processing in life science.", "doi": "10.1017/S2633903X24000114", "pmid": "39776608", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC11704127"}, {"db": "pii", "key": "S2633903X24000114"}], "notes": [], "created": "2026-08-20T08:08:11.844Z", "modified": "2026-08-20T08:08:12.022Z"}, {"entity": "publication", "iuid": "c89237bd61794c2aa244bb46bbc6c899", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/c89237bd61794c2aa244bb46bbc6c899.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/c89237bd61794c2aa244bb46bbc6c899"}}, "title": "Machine learning in microscopy - insights, opportunities and challenges.", "authors": [{"family": "Cunha", "given": "In\u00eas", "initials": "I", "orcid": "0000-0002-1327-9018", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/77f0773f41cc4f1191aaba0d21ec745a.json"}}, {"family": "Latron", "given": "Emma", "initials": "E"}, {"family": "Bauer", "given": "Sebastian", "initials": "S"}, {"family": "Sage", "given": "Daniel", "initials": "D", "orcid": "0000-0002-1150-1623", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/919b0180005444d88757db8e63b80d83.json"}}, {"family": "Griffi\u00e9", "given": "Juliette", "initials": "J", "orcid": "0000-0001-6438-0119", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2650e9aa00f7499aba2f222c49a95e71.json"}}], "type": "journal article", "published": "2024-10-15", "journal": {"title": "J. Cell. Sci.", "issn": "1477-9137", "volume": "137", "issue": "20", "issn-l": "0021-9533"}, "abstract": "Machine learning (ML) is transforming the field of image processing and analysis, from automation of laborious tasks to open-ended exploration of visual patterns. This has striking implications for image-driven life science research, particularly microscopy. In this Review, we focus on the opportunities and challenges associated with applying ML-based pipelines for microscopy datasets from a user point of view. We investigate the significance of different data characteristics - quantity, transferability and content - and how this determines which ML model(s) to use, as well as their output(s). Within the context of cell biological questions and applications, we further discuss ML utility range, namely data curation, exploration, prediction and explanation, and what they entail and translate to in the context of microscopy. Finally, we explore the challenges, common artefacts and risks associated with ML in microscopy. Building on insights from other fields, we propose how these pitfalls might be mitigated for in microscopy.", "doi": "10.1242/jcs.262095", "pmid": "39465533", "labels": [], "xrefs": [{"db": "pii", "key": "362505"}], "notes": [], "created": "2026-08-20T12:38:31.317Z", "modified": "2026-08-20T12:38:31.404Z"}, {"entity": "publication", "iuid": "1764d34dcf0c4b99ae17a97dac0204f0", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/1764d34dcf0c4b99ae17a97dac0204f0.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/1764d34dcf0c4b99ae17a97dac0204f0"}}, "title": "Bridging the Gap: Integrating Cutting-edge Techniques into Biological Imaging with deepImageJ", "authors": [{"family": "Fuster-Barcel\u00f3", "given": "Caterina", "initials": "C", "orcid": "0000-0002-4784-6957", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/881d36fa227445c9b381a03990b5ed49.json"}}, {"family": "Garc\u00eda L\u00f3pez de Haro", "given": "Carlos", "initials": "C"}, {"family": "G\u00f3mez-de-Mariscal", "given": "Estibaliz", "initials": "E", "orcid": "0000-0003-2082-3277", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cdcc04b3b1534abc8457380935970dd5.json"}}, {"family": "Ouyang", "given": "Wei", "initials": "W"}, {"family": "Olivo-Marin", "given": "Jean Christophe", "initials": "JC", "orcid": "0000-0001-6796-0696", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f2558d56edd54d0e87904930c8ec54f6.json"}}, {"family": "Sage", "given": "Daniel", "initials": "D", "orcid": "0000-0002-1150-1623", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/919b0180005444d88757db8e63b80d83.json"}}, {"family": "Mu\u00f1oz-Barrutia", "given": "Arrate", "initials": "A", "orcid": "0000-0002-1573-1661", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1f71dcceb0284105a7033f2ed195b9c1.json"}}], "type": "posted-content", "published": "2024-01-15", "journal": {"issn-l": null}, "abstract": null, "doi": "10.1101/2024.01.12.575015", "pmid": null, "labels": [], "xrefs": [], "notes": [], "created": "2026-08-20T10:51:31.337Z", "modified": "2026-08-20T10:51:31.417Z"}, {"entity": "publication", "iuid": "b6a2fcc71d4e4edd8d9d294fe075227c", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/b6a2fcc71d4e4edd8d9d294fe075227c.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/b6a2fcc71d4e4edd8d9d294fe075227c"}}, "title": "JDLL: a library to run deep learning models on Java bioimage informatics platforms.", "authors": [{"family": "Garc\u00eda L\u00f3pez de Haro", "given": "Carlos", "initials": "C"}, {"family": "Dallongeville", "given": "St\u00e9phane", "initials": "S", "orcid": "0000-0002-2204-7083", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/6d1860b509cf415fa512a4286d25c9f8.json"}}, {"family": "Musset", "given": "Thomas", "initials": "T"}, {"family": "G\u00f3mez-de-Mariscal", "given": "Estibaliz", "initials": "E", "orcid": "0000-0003-2082-3277", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cdcc04b3b1534abc8457380935970dd5.json"}}, {"family": "Sage", "given": "Daniel", "initials": "D", "orcid": "0000-0002-1150-1623", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/919b0180005444d88757db8e63b80d83.json"}}, {"family": "Ouyang", "given": "Wei", "initials": "W", "orcid": "0000-0002-0291-926X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/56f601e1d1c6448aab0c3a1e3cffdbfc.json"}}, {"family": "Mu\u00f1oz-Barrutia", "given": "Arrate", "initials": "A", "orcid": "0000-0002-1573-1661", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1f71dcceb0284105a7033f2ed195b9c1.json"}}, {"family": "Tinevez", "given": "Jean-Yves", "initials": "JY", "orcid": "0000-0002-0998-4718", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/83f54be6102d4b4499ac702b1289e9c7.json"}}, {"family": "Olivo-Marin", "given": "Jean-Christophe", "initials": "JC", "orcid": "0000-0001-6796-0696", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f2558d56edd54d0e87904930c8ec54f6.json"}}], "type": "letter", "published": "2024-01-00", "journal": {"title": "Nat. Methods", "issn": "1548-7105", "volume": "21", "issue": "1", "pages": "7-8", "issn-l": "1548-7091"}, "abstract": null, "doi": "10.1038/s41592-023-02129-x", "pmid": "38191929", "labels": [], "xrefs": [{"db": "pii", "key": "10.1038/s41592-023-02129-x"}], "notes": [], "created": "2026-08-20T09:02:58.057Z", "modified": "2026-08-20T09:02:58.221Z"}, {"entity": "publication", "iuid": "fb8612db660c481d9be4a872507534e2", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/fb8612db660c481d9be4a872507534e2.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/fb8612db660c481d9be4a872507534e2"}}, "title": "DeepImageJ: A user-friendly environment to run deep learning models in ImageJ.", "authors": [{"family": "G\u00f3mez-de-Mariscal", "given": "Estibaliz", "initials": "E", "orcid": "0000-0003-2082-3277", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cdcc04b3b1534abc8457380935970dd5.json"}}, {"family": "Garc\u00eda-L\u00f3pez-de-Haro", "given": "Carlos", "initials": "C"}, {"family": "Ouyang", "given": "Wei", "initials": "W"}, {"family": "Donati", "given": "Laur\u00e8ne", "initials": "L"}, {"family": "Lundberg", "given": "Emma", "initials": "E", "orcid": "0000-0001-7034-0850", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bb07e6d0122c4528a927c1fe922e3bc8.json"}}, {"family": "Unser", "given": "Michael", "initials": "M"}, {"family": "Mu\u00f1oz-Barrutia", "given": "Arrate", "initials": "A", "orcid": "0000-0002-1573-1661", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1f71dcceb0284105a7033f2ed195b9c1.json"}}, {"family": "Sage", "given": "Daniel", "initials": "D", "orcid": "0000-0002-1150-1623", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/919b0180005444d88757db8e63b80d83.json"}}], "type": "journal article", "published": "2021-10-00", "journal": {"title": "Nat. Methods", "issn": "1548-7105", "volume": "18", "issue": "10", "pages": "1192-1195", "issn-l": "1548-7091"}, "abstract": "DeepImageJ is a user-friendly solution that enables the generic use of pre-trained deep learning models for biomedical image analysis in ImageJ. The deepImageJ environment gives access to the largest bioimage repository of pre-trained deep learning models (BioImage Model Zoo). Hence, nonexperts can easily perform common image processing tasks in life-science research with deep learning-based tools including pixel and object classification, instance segmentation, denoising or virtual staining. DeepImageJ is compatible with existing state of the art solutions and it is equipped with utility tools for developers to include new models. Very recently, several training frameworks have adopted the deepImageJ format to deploy their work in one of the most used softwares in the field (ImageJ). Beyond its direct use, we expect deepImageJ to contribute to the broader dissemination and reuse of deep learning models in life sciences applications and bioimage informatics.", "doi": "10.1038/s41592-021-01262-9", "pmid": "34594030", "labels": [], "xrefs": [{"db": "pii", "key": "10.1038/s41592-021-01262-9"}], "notes": [], "created": "2026-08-20T09:02:47.487Z", "modified": "2026-08-20T09:02:47.558Z"}]}