Publication:
Building a bioimage analysis workflow using deep learning

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2022
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Springer Nature
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The aim of this workflow is to quantify the morphology of pancreatic stem cells lying on a 2D polystyrene substrate from phase contrast microscopy images. For this purpose, the images are first processed with a Deep Learning model trained for semantic segmentation (cell/background); next, the result is refined and individual cell instances are segmented before characterizing their morphology. Through this workflow the readers will learn the nomenclature and understand the principles of Deep Learning applied to image processing.
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Gómez-De-Mariscal, E., Franco-Barranco, D., Muñoz-Barrutia, A., & Arganda-Carreras, I. (2022). Building a Bioimage Analysis Workflow Using Deep Learning. In K. Miura & N. Sladoje (Ed.), Bioimage data analysis workflows ‒ Advanced components and methods (59-88). Springer Nature