Bridging the gap: Integrating cutting-edge techniques into biological imaging with deepImageJ
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 dem...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2024-01-01
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| Series: | Biological Imaging |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2633903X24000114/type/journal_article |
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| author | Caterina Fuster-Barceló Carlos García-López-de-Haro Estibaliz Gómez-de-Mariscal Wei Ouyang Jean-Christophe Olivo-Marin Daniel Sage Arrate Muñoz-Barrutia |
| author_facet | Caterina Fuster-Barceló Carlos García-López-de-Haro Estibaliz Gómez-de-Mariscal Wei Ouyang Jean-Christophe Olivo-Marin Daniel Sage Arrate Muñoz-Barrutia |
| author_sort | Caterina Fuster-Barceló |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-51325f00c74b43acad8f4da92d62e6e4 |
| institution | Kabale University |
| issn | 2633-903X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Biological Imaging |
| spelling | doaj-art-51325f00c74b43acad8f4da92d62e6e42024-11-22T06:20:20ZengCambridge University PressBiological Imaging2633-903X2024-01-01410.1017/S2633903X24000114Bridging the gap: Integrating cutting-edge techniques into biological imaging with deepImageJCaterina Fuster-Barceló0https://orcid.org/0000-0002-4784-6957Carlos García-López-de-Haro1Estibaliz Gómez-de-Mariscal2Wei Ouyang3Jean-Christophe Olivo-Marin4https://orcid.org/0000-0001-6796-0696Daniel Sage5https://orcid.org/0000-0002-1150-1623Arrate Muñoz-Barrutia6https://orcid.org/0000-0002-1573-1661Bioengineering Department[CMT1], Universidad Carlos III de Madrid, Leganes, Spain Bioengineering Division, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, SpainBiological Image Analysis Unit, Institut Pasteur, Paris, FranceOptical Cell Biology Group, Instituto Gulbenkian de Ciência, Oeiras, PortugalScience for Life Laboratory, Department of Applied Physics, KTH Royal Institute of Technology, Stockholm, SwedenBiological Image Analysis Unit, Institut Pasteur, Centre National de la Reserche Scientifique UMR3691, Université Paris Cité, París, FranceBiomedical Imaging Group and Center for Imaging, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, SwitzerlandBioengineering Department[CMT1], Universidad Carlos III de Madrid, Leganes, Spain Bioengineering Division, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, SpainThis 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.https://www.cambridge.org/core/product/identifier/S2633903X24000114/type/journal_articleBioImage model zoobiological imagingImageJJDLL |
| spellingShingle | Caterina Fuster-Barceló Carlos García-López-de-Haro Estibaliz Gómez-de-Mariscal Wei Ouyang Jean-Christophe Olivo-Marin Daniel Sage Arrate Muñoz-Barrutia Bridging the gap: Integrating cutting-edge techniques into biological imaging with deepImageJ Biological Imaging BioImage model zoo biological imaging ImageJ JDLL |
| title | Bridging the gap: Integrating cutting-edge techniques into biological imaging with deepImageJ |
| title_full | Bridging the gap: Integrating cutting-edge techniques into biological imaging with deepImageJ |
| title_fullStr | Bridging the gap: Integrating cutting-edge techniques into biological imaging with deepImageJ |
| title_full_unstemmed | Bridging the gap: Integrating cutting-edge techniques into biological imaging with deepImageJ |
| title_short | Bridging the gap: Integrating cutting-edge techniques into biological imaging with deepImageJ |
| title_sort | bridging the gap integrating cutting edge techniques into biological imaging with deepimagej |
| topic | BioImage model zoo biological imaging ImageJ JDLL |
| url | https://www.cambridge.org/core/product/identifier/S2633903X24000114/type/journal_article |
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