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Using Aivia Cloud

Aivia Cloud creating your own deep learning model starts with two (sets of) images - raw data and example data (aka ground truth).

For image segmentation tasks the ground truth data is composed of your annotations which can be created using one of Aivia's image analysis recipes, using one of the intelligent semi-automatic tools in Aivia or can be imported from other software tools. The raw data are the images as they were acquired by the microscope.

For restoration tasks the ground truth data is a set of data which represents the absolute best quality images (for that set up/experiment) that can be generated. The raw data is composed of images created using imaging parameters which are very desirable (e.g. very low laser power or very fast acquisition) but that can only generate sub-par images.

For prediction tasks the ground truth data is often a set of images which show the localization of a fluorescently tagged protein or organelle. The raw data is often composed of images created using a different imaging modality (e.g. bright-field) which the user wishes to use more routinely for any number of reasons (e.g. cost, photo-toxicity, fluorescence labeling complexity)

Ground Truth Generation

Aivia Cloud 

Important Information

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