A Cell Derived Active Contour (CDAC) Method for Robust Tracking in Low Frame Rate, Low Contrast Phase Microscopy - an Example: The Human hNT Astrocyte

Abstract

The problem of automated segmenting and tracking of the outlines of cells in microscope images is the subject of active research. While great progress has been made on recognizing cells that are of high contrast and of predictable shape, many situations arise in practice where these properties do not exist and thus many interesting potential studies - such as the migration patterns of astrocytes to scratch wounds - have been relegated to being largely qualitative in nature. Here we analyse a select number of recent developments in this area, and offer an algorithm based on parametric active contours and formulated by taking into account cell movement dynamics. This Cell-Derived Active Contour (CDAC) method is compared with two state-of-the-art segmentation methods for phase-contrast microscopy. Specifically, we tackle a very difficult segmentation problem: human astrocytes that are very large, thin, and irregularly-shaped. We demonstrate quantitatively better results for CDAC as compared to similar segmentation methods, and we also demonstrate the reliable segmentation of qualitatively different data sets that were not possible using existing methods. We believe this new method will enable new and improved automatic cell migration and movement studies to be made.

ImaEdge – a platform for quantitative analysis of the spatiotemporal dynamics of cortical proteins during cell polarization

“…In the case of these proteins, segmentation using a single threshold value cannot capture the proper cell edges for the entire duration of cell polarization, during which the intensities and patterns of cortical proteins will change. These problems could be addressed in principle by using segmentation methods based on the progressive evolution of an active contour that is defined either by the minimization of a customized energy function or by parameterfree methods that track the local information from the cell edge (Ersoy et al, 2008a,b;Li et al, 2009;Nejati Javaremi et al, 2013;Song et al, 2017;Wu et al, 2015). These methods in practice involve relatively complex segmentation algorithms with multiple key input parameters, requiring significant user intervention to analyze large-scale datasets from long time-lapse movies, such as those obtained during cell polarization.…”