DeepImageJ: Bridging Deep Learning to ImageJ
E. Gomez-de-Mariscal, C. Garcia-Lopez-de-Haro, A. Munoz-Barrutia, D. Sage
European Light Microscopy Initiative 2021 (elmi'21), Virtual, June 22-25, 2021.
In the last decade, the use of Deep Learning (DL) methodologies has made a vast improvement in the solution of several bioimage analysis tasks such as denoising, super-resolution, segmentation, detection, tracking, response prediction, or computer-aided diagnosis [2]. These techniques support automatic image processing workflows and have demonstrated potential to surpass human-level performance in common tasks. Consequently, they have a profound impact on the way life-science researchers conduct their bioimage data analysis [1]. Nonetheless, the integration of this breakthrough technology into life-science research pipelines remains still a challenge for the scientific community. Training and evaluating DL models requires previous programming expertise and technical knowledge about Machine Learning. Therefore, the transfer of this technology to the daily practice of life sciences researchers remains a bottleneck. Aware of the latter situation, there are already some pioneer works that target the very need of making DL solutions accessible through user-friendly software [3, 4, 5, 6, 7]. Moreover, there is an increasing interest in the bioimage analysis community to teach and learn some general knowledge about DL and democratize it.
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C. Stringer, T. Wang, M. Michaelos, M. Pachitariu, "Cellpose: A Generalist Algorithm for Cellular Segmentation," Nature Methods, vol. 18, no. 1, pp. 100-106, January 2021.
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AUTHOR="Gomez-de-Mariscal, E. and Garcia-Lopez-de-Haro, C. and
Munoz-Barrutia, A. and Sage, D.",
TITLE="{DeepImageJ}: {B}ridging Deep Learning to {ImageJ}",
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