Estimation of rheological parameters for unstained living cells

TL;DR

This paper introduces an automated video analysis method to estimate intracellular flow velocities and rheological parameters in unstained living cells, combining feature detection, motion separation, and physical modeling.

Contribution

It presents a novel approach for extracting intracellular flow and viscosity parameters from unstained cell videos using SURF features and covariance-based motion separation.

Findings

Parameters agree with literature data

Effective viscosity and diffusion coefficients estimated

Method successfully applied to osteoblast and hepatocyte videos

Abstract

In video-records, objects moving in intracellular regions are often hardly detectable and identifiable. In order to squeeze the information on the intracellular flows, we propose an automatic method of reconstruction of intracellular flow velocity fields based only on a recorded video of an unstained cell. The basis of the method is detection of speeded-up robust features (SURF) and assembling them into trajectories. Two components of motion -- direct and Brownian -- are separated by an original method based on minimum covariance estimation. The Brownian component gives a spatially resolved diffusion coefficient. The directed component yields a velocity field, and, after fitting the vorticity equation, estimation of the spatially distributed effective viscosity. The method was applied to videos of a human osteoblast and a hepatocyte. The obtained parameters are in agreement with literature data.