Speaker
Description
The translation of raw data into quantitative information, which is the ultimate goal of microscopy-based research studies, requires the implementation of standardized data pipelines to process and analyze the measured images. Image quality assessment (IQA) is an essential ingredient for the validation of each intermediate result, but it frequently relies on ground-truth images, visual perception, and complex quality metrics, highlighting the need for interpretable, automatic and standardized methods for image evaluation. We present a workflow for the integration of quality metrics into machine learning models to obtain automatic IQA and artifacts identification. In our study, a classification model is trained with no-reference quality metrics to discern high quality images and measurements affected by experimental artifacts, and it is utilized to predict the presence of artifacts and the image quality in unseen datasets. We present an application of our method to the Evaluation and Enhancement (EVEN) of uneven illumination corrections for multimodal microscopy. We show that our method is easily interpretable and that it can be used for the generation of quality rankings and the optimization of processed images.
| Authors | Elena Corbetta*, Thomas Bocklitz, Matteo Calvarese, Hyeonsoo Bae, Chenting Lai, David Pertzborn, Tobias Meyer-Zedler, Bernhard Messerschmidt, Anna Mühlig, Orlando Guntinas-Lichius, Michael Schmitt, Juergen Popp |
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| Keywords | optical microscopy, image quality assessment, machine learning |
