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Bridging the gap: Integrating cutting-edge techniques into biological imaging with deepImageJ.

Caterina Fuster-Barceló1,2, Carlos García-López-de-Haro3, Estibaliz Gómez-de-Mariscal4

  • 1Bioengineering Department[CMT1], Universidad Carlos III de Madrid, Leganes, Spain.

Biological Imaging
|January 8, 2025
PubMed
Summary

DeepImageJ, a Fiji/ImageJ plugin, now supports multiple deep learning frameworks for advanced bioimage analysis. This update enhances capabilities for complex pipelines, 3D analysis, and large image processing in life sciences.

Keywords:
BioImage model zooImageJJDLLbiological imaging

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Area of Science:

  • Life Sciences
  • Bioimage Analysis
  • Computational Biology

Background:

  • Fiji/ImageJ is a widely used platform for life science image analysis.
  • Deep learning (DL) offers powerful tools for complex image processing tasks.
  • Integrating DL into existing bioimage analysis workflows can be challenging.

Purpose of the Study:

  • To present advancements in deepImageJ, a Fiji/ImageJ plugin for bioimage analysis.
  • To demonstrate the plugin's enhanced capabilities in deploying deep learning models.
  • To showcase streamlined workflows for complex, 3D, and large-scale image analysis.

Main Methods:

  • Integration of the Java Deep Learning Library for compatibility with TensorFlow, PyTorch, and ONNX.
  • Demonstration of deepImageJ's ability to run multiple DL engines within Fiji/ImageJ.
  • Application of deepImageJ in three case studies: image-to-image translation with nuclei segmentation, 3D nuclei segmentation, and large volume segmentation.

Main Results:

  • DeepImageJ now supports diverse deep learning frameworks, enhancing its flexibility.
  • The plugin successfully handles complex pipelines, 3D image analysis, and large image volumes.
  • Case studies confirm the effectiveness of deepImageJ for integrated and advanced bioimage segmentation tasks.
  • Compatibility with the BioImage Model Zoo is established.

Conclusions:

  • Advancements in deepImageJ provide a more flexible and user-friendly framework for bioimage analysis.
  • The plugin makes advanced deep learning more accessible and efficient for life science researchers.
  • DeepImageJ is poised to enable next-generation image processing in the life sciences.