Resolving and Quantifying Viral-Like Particles via Blind Deconvolution
Viruses represent the most numerous biological entities on Earth; but the direct quantification of viruses within ecosystems reminds an ongoing challenge. The classical method of epifluorescence microscopy (EFM) reminds the gold standard measurement of viral-like particles (VLPs) within ecosystems. Quantifying VLPs in epifluorescence microscopy is burdened by ongoing challenges that include manual human counting, an absence of accurate morphological sizing, and the a range of viral sizes (20-300 nm) falling below the diffraction limit of light microscopy. Here, a proof-of-concept computer vision framework for the automated enumeration and sizing of viral-like particles is presented, known as EpiVirQuant. A novel tunable pointspread function is introduced which allows for a dynamic blind deconvolution. Final enumeration by EpiVirQuant was directly compared to manual human counting which yielded 18% more VLPs identified. EpiVirQuant quantified average VLP size of 179.5 nm, which is consistent with median size of VLPs in nature of of _160 nm. Runtime ranged from 60-80 seconds-perimage depending on parameter selection. This provides a viable proof-of-concept cost-effective solution for the enumeration and large-scale morphological analysis of VLPs.