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Bridging Deep Learning to ImageJ

A. Muñoz Barrutia, D. Sage

Community Workshop: Bioimage Analysis Open-Source Software Tools, Conference on Spanish & Portuguese Advanced Optical Microscopy (SPAOM'19), Coimbra, Portuguese Republic, November 6-8, 2019.



Machine learning (ML), and in particular, Deep Neural Networks (DNN), have become an inflexion point in many areas of scientific research. In the case of biomedical image analysis, these new techniques provide significant improvements in most of the tasks such as denoising, super-resolution, segmentation, detection, tracking, response prediction or computer aided diagnosis.

Nonetheless, the use of DNN models requires previous programming knowledge and expertise, which makes them unapproachable to the general public. Therefore, the spread of this technology to the scientific community is strongly limited. In this workshop, we present a friendly interface for the use of these models that have been developed in collaboration between EPFL and UC3M. This ready-to-use ImageJ/FIJI plugin has the potential to make available many of the powerful algorithms for image processing that are continuously being developed and published, enhancing research.


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