Hessian-based quantitative image analysis of host-pathogen confrontation assays

Area of Science:

• Microbiology
• Cell Biology
• Biomedical Imaging

Background:

• Host-fungus interactions are critical in infectious diseases, often requiring advanced imaging techniques.
• Current methods for studying these interactions, like phagocytosis assays, frequently rely on fluorescent labeling, which can be time-consuming and potentially affect cellular functions.

Purpose of the Study:

• To develop and validate a novel, label-free image processing method for quantifying host-fungus interactions.
• To reduce the reliance on fluorescence-based imaging in studying cellular responses to fungal pathogens.

Main Methods:

• Utilized transmitted light bright field microscopy images.
• Applied Hessian matrix eigenvalues for image segmentation to differentiate host cells (macrophages) and fungal conidia from the background.
• Compared the algorithm's performance against a fluorescence-based imaging approach using the same dataset.

Main Results:

• The new algorithm accurately segmented and characterized unlabeled host cells and fungal conidia.
• Performance was comparable to traditional fluorescence-based methods.
• Demonstrated the potential for label-free segmentation of fungal conidia.

Conclusions:

• The developed image processing technique provides a viable, label-free alternative for studying host-fungus interactions.
• This method reduces experimental time and cost associated with fluorescent labeling.
• Minimizes potential artifacts and side effects of fluorescence labeling on biological processes.