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bioRxiv · 10.1101/2025.02.26.640237

Developing the Computational Image Processing Methodfor Quantitative Analysis of Nanopore Structure Obtained from HS-AFM (AFMnanoQ)

Nguyen, T. V. T.·Ly, N. Q.·Le, N. T. P.·Nguyen, H. D.·Ngo, K. X.
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Abstract

High-Speed Atomic Force Microscopy (HS-AFM) enables imaging of biological structures and dynamics with nanometer spatial and millisecond temporal resolution. AFM images contain three-dimensional (3D) surface information, comprising two-dimensional (2D) lateral (x-y) and one-dimensional (1D) height (z) encoded in pixel intensity. This dynamic structure poses significant challenges for instance boundary detection and morphological analysis. To address this, we develop AFMnanoSALQ, a feature-driven computational framework for semi-automatic labeling and quantitative (SALQ) detection and morphological measurement of HS-AFM data. Unlike conventional methods that rely solely on either visual or geometric features for 2D boundary detection, AFM- nanoSALQ integrates both to extract 3D morphology. It requires neither annotated data nor intensive training, enabling fast deployment at minimal cost. With performance comparable to typical deep-learning models, AFMnanoSALQ facilitates semi-automatic labeling, making it a practical tool for preliminary data inspection and accelerating the creation of training datasets. As a case study, we focus on -hemolysin (HL), a {beta}-barrel pore-forming toxin secreted by Staphylococcus aureus, using both synthetic and experimental AFM data. AFMnanoSALQ provides a foundation for future deep learning studies, enabling both dataset generation and cross-validation between feature-driven and data-driven approaches.

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Nguyen, T. V. T., Ly, N. Q., Le, N. T. P., Nguyen, H. D., Ngo, K. X.. 2025-03-02. Developing the Computational Image Processing Methodfor Quantitative Analysis of Nanopore Structure Obtained from HS-AFM (AFMnanoQ). https://doi.org/10.1101/2025.02.26.640237

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