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 the life sciences. The plugin, known for its user-friendly interface, facilitates the application of diverse pre-trained neural networks to custom data. The manuscript demonstrates a number of deepImageJ capabilities, particularly in executing complex pipelines, 3D analysis, and processing large images. A key development is the integration of the Java Deep Learning Library (JDLL), expanding deepImageJ's compatibility with various deep learning frameworks, including TensorFlow, PyTorch, and ONNX. This allows for running multiple engines within a single Fiji/ImageJ instance, streamlining complex bioimage analysis tasks. The manuscript details three case studies to demonstrate these capabilities. The first explores integrated image-to-image translation and nuclei segmentation. The second focuses on 3D nuclei segmentation. The third case study deals with large image segmentation. These studies underscore deepImageJ's versatility and power in bioimage analysis, emphasizing its role as a critical tool for life scientists and researchers. The advancements in deepImageJ bridge the gap between deep learning model developers and end-users, enabling a more accessible and efficient approach to biological image analysis. ### Competing Interest Statement The authors have declared no competing interest.