Work

Machine Learning‐Based Estimation of Experimental Artifacts and Image Quality in Fluorescence Microscopy

Year: 2024

Type: article

Abstract: Reliable characterization of image data is fundamental for imaging applications, FAIR data management, and an objective evaluation of image acquisition, processing, and analysis steps in an image‐based investigation of biological samples. Image quality assessment (IQA) often relies on human visual perception, which is not objective, or reference ground truth images, which are not often available. This study presents a method for a comprehensive IQA of microscopic images, which solves these issues by employing a set of reference‐free metrics that estimate the presence of experimental artifacts. The metrics are jointly validated on a semisynthetic dataset and are tested on experimental images. Finally, the metrics are employed in a machine learning model, demonstrating their effectiveness for automatic artifact classification through multimarker IQA. This work provides a reliable reference‐free method for IQA in optical microscopy, which can be integrated into the experimental workflow and tuned to address specific artifact detection tasks.

Source: Advanced Intelligent Systems

DOI: 10.1002/aisy.202400491

Authors: Elena Corbetta, Thomas Bocklitz

Institutions: Leibniz Institute of Photonic Technology, Friedrich Schiller University Jena, University of Bayreuth

Language: English

Topic: Image and Video Quality Assessment

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,510