deepImageJ

deepImageJ integrates pre-trained convolutional neural networks into Fiji/ImageJ for bioimage analysis, supporting diverse deep learning frameworks and complex pipeline deployments.

Key Features:

• Multi-framework compatibility: Integrates the Java Deep Learning Library to support TensorFlow, PyTorch, and ONNX, allowing multiple engines to run within a single Fiji/ImageJ instance.
• 3D image analysis: Processes three-dimensional image data for nuclei segmentation and other volumetric analyses.
• Large image processing: Handles large image volumes for segmentation tasks.
• Complex pipeline deployment: Enables integrated image-to-image translation followed by downstream segmentation in sequential workflows.
• BioImage Model Zoo integration: Maintains compatibility with the BioImage Model Zoo for accessing and deploying community-contributed models.

Scientific Applications:

• Integrated image-to-image translation and nuclei segmentation: Applies pre-trained convolutional neural networks for sequential image transformation followed by nuclei detection and segmentation.
• 3D nuclei segmentation: Performs volumetric segmentation of nuclei in three-dimensional biological image data.
• Large image volume segmentation: Segments extensive image datasets that exceed standard memory constraints.

Methodology:

deepImageJ integrates the Java Deep Learning Library to enable running multiple deep learning engines within a single Fiji/ImageJ instance, deploying pre-trained convolutional neural networks for image-to-image translation and segmentation tasks.