- :J. Ritu, T. Jefferis, C. Sayes and, J. Peeples, “Morphological Change Detection for Scanning Electron Microscope Images of Lung Cell Surfaces,” in International Conference on Advances in Artificial Intelligence and Machine Learning (AAIML), 2026, in Press.
- Abstract: 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). An adaptation of the Segment Anything Model (SAM) tailored for microscopy images, 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. Together, these 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. This framework provides biologically grounded insights with direct relevance for automated toxicology assessment and monitoring of nanoparticle-induced cellular changes. The code for this work is publicly available: https://github.com/Advanced-Vision-and-Learning-Lab/SEM_Morphology.
- Link: TBD
- Publication date: March 2026
- Citation: J. Ritu, T. Jefferis, C. Sayes and, J. Peeples, “Morphological Change Detection for Scanning Electron Microscope Images of Lung Cell Surfaces,” in International Conference on Advances in Artificial Intelligence and Machine Learning (AAIML), 2026, in Press.