QUAL-IF-AI: Quality Control of Immunofluorescence Images using Artificial Intelligence

Abstract

Fluorescent imaging has revolutionized biomedical research, enabling the study of intricate cellular processes. Multiplex immunofluorescent imaging has extended this capability, permitting the simultaneous detection of multiple markers within a single tissue section. However, these images are susceptible to a myriad of undesired artifacts, which compromise the accuracy of downstream analyses. Manual artifact removal is impractical given the large number of images generated in these experiments, necessitating automated solutions. Here, we present QUAL-IF-AI, a multi-step deep learning-based tool for automated artifact identification and management. We demonstrate the utility of QUAL-IF-AI in detecting four of the most common types of artifacts in fluorescent imaging: air bubbles, tissue folds, external artifacts, and out-of-focus areas. We show how QUAL-IF-AI outperforms state-of-the-art methodologies in a variety of multiplexing platforms achieving over 85% of classification accuracy and more than 0.6 Intersection over Union (IoU) across all artifact types. In summary, this work presents an automated, accessible, and reliable tool for artifact detection and management in fluorescent microscopy, facilitating precise analysis of multiplexed immunofluorescence images.