Automatic optimization of flat-field corrections by evaluation and enhancement (EVEN) in multimodal optical microscopy

Authors:

Elena Corbetta, Matteo Calvarese, Patrick Then, Hyeonsoo Bae, Tobias Meyer-Zedler, Bernhard Messerschmidt, Orlando Guntinas-Lichius, Michael Schmitt, Christian Eggeling, Juergen Popp, Thomas Bocklitz

Keywords:

EVEN; Evaluation and Enhancement; Linear Discriminant Analysis; multimodal nonlinear imaging; Computational science; Fluorescence imaging; Image processing; Machine learning

Abstract:

Uneven illumination affects all images acquired by optical microscopes, especially large, multicolour and nonlinear measurements. Although removal is possible with various algorithms, evaluating raw and processed images is challenging due to the lack of established workflows for image quality assessment. This manuscript describes a machine learning-based method, EVEN (Evaluation and Enhancement), to assess and optimise corrections in optical microscopy. EVEN integrates quantitative image metrics into a Linear Discriminant Analysis model to detect and predict image quality, automatically optimising corrections. The method can be integrated into the optical microscopy pipeline to simplify further processing and analysis. Here, we show the implementation and application of EVEN in different processing scenarios, including multimodal nonlinear imaging of human and neck tissue slices and multichannel fluorescence measurements of stained cells, demonstrating its capability to automatically optimise image quality by assessing single-channel corrections.

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