Morphological Change Detection for Scanning Electron Microscope Images of Lung Cell Surfaces

Exposure to nanoparticles can alter cellular morphology, which serves as an indicator of toxic response and can be visualized using scanning electron microscopy (SEM). Texture-based features have been widely used to quantify nanoscale surface complexity, but they primarily operate at the pixel level. To provide complementary biological context, this study introduces a morphology-driven SEM analysis framework that extracts interpretable features from both cells and their protrusions (cilia or dendrites). Micro Segment Anything Model (MicroSAM) is used for semantic segmentation to quantify cell-level (e.g., area, circularity) and dendrite-level features (e.g., length, waviness). Statistical tests and effect size analysis identify significant differences across exposure groups, while correlation analysis reveals inter-feature relationships. Morphology-based phenotyping of dendritic structures highlights exposure-specific alterations. The results demonstrate interpretable patterns of structural changes across nanoparticle types, offering biologically grounded insights that enhance and extend texture-based analysis through morphology-aware characterization.