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

## TL;DR

The EVEN method uses machine learning to automatically improve image quality in optical microscopy by correcting uneven illumination.

## Contribution

EVEN introduces a machine learning workflow for automatic image correction and quality assessment in multimodal optical microscopy.

## Key findings

- EVEN integrates image metrics into a Linear Discriminant Analysis model to detect and predict image quality.
- The method was successfully applied to multimodal nonlinear imaging and multichannel fluorescence measurements.
- EVEN simplifies further processing by automatically optimizing corrections in single-channel images.

## 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.

The Evaluation and Enhancement (EVEN) method optimizes automatically the quality of multichannel microscopy images affected by uneven illumination, enabling accurate visual interpretation and image analysis in challenging imaging scenarios.