Machine-learning-guided quantitative delineation of cell morphological features and responses to nanomaterials
Kenry Kenry
Abstract
-means clustering of morphological features reveal the possible existence of heterogenous cell subpopulations and treatment responses among the seemingly homogenous cell groups. This shows the merit of the reported approach in complementing conventional techniques for cell analysis. It is anticipated that the demonstrated method will further aid the implementation of machine learning to streamline the analysis of cell morphology and responses for early disease diagnosis and treatment response monitoring.
Topics & Concepts
Phase imagingPhase contrast microscopyContrast (vision)MicroscopyCellArtificial intelligenceComputer scienceMaterials scienceNanotechnologyBiologyMedicinePathologyOpticsPhysicsGeneticsCell Image Analysis TechniquesAdvanced Fluorescence Microscopy TechniquesDigital Holography and Microscopy