Cell deformability heterogeneity recognition by unsupervised machine learning from in-flow motion parameters
Maria Isabella Maremonti, David Dannhauser, Valeria Panzetta, Paolo A. Netti, Filippo Causa
Abstract
). These motion parameters clearly defined cell clusters in terms of motion regimes corresponding to specific deformability. Such correlation is verified in a wide range of rheological/mechanical properties from the elastic cells moving like R until the almost viscous cells moving as TT. Thus, our approach shows how simple motion parameters allow cell deformability heterogeneity recognition, directly measuring rheological/mechanical properties.
Topics & Concepts
Motion (physics)Artificial intelligenceComputer scienceFlow (mathematics)Computer visionMechanicsPhysicsMicrofluidic and Bio-sensing TechnologiesBlood properties and coagulationCell Image Analysis Techniques