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Cellprofiler output during processing software#
We showcase the versatility of MOrgAna on several in vitro systems, each imaged with a different microscope, thus demonstrating the wide applicability of the software to diverse organoid types and biomedical studies.
![cellprofiler output during processing cellprofiler output during processing](https://os.bio-protocol.org/attached/image/20190101/20190101201311_5195.jpg)
Although the MOrgAna interface is developed for users with little to no programming experience, its modular structure makes it a customizable package for advanced users. Here, we present MOrgAna, a Python-based software that implements machine learning to segment images, quantify and visualize morphological and fluorescence information of organoids across hundreds of images, each with one object, within minutes. Hence, there is a pressing demand for a coding-free, intuitive and scalable solution that analyses such image data in an automated yet rapid manner.
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The large volumes of images, resulting from hundreds of organoids cultured at once, are becoming increasingly difficult to inspect and interpret. Organoids are large structures with high phenotypic complexity and are imaged on a wide range of platforms, from simple benchtop stereoscopes to high-content confocal-based imaging systems. Recent years have seen a dramatic increase in the application of organoids to developmental biology, biomedical and translational studies.
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