Ceulular Imaging Data Analysis: Mircotubule Dynamics in Living Cell

Microtubules are dynamic polymers that rapidly transition between states of growth, shortening, and pause. These dynamic events are critical for studying cellular processes such as the cancer drug effectiveness study. Typically, these events are quantified by imaging microtubule movements over time, which results in large data sets that require rigorous quantitative analysis. In most cases, the analysis was performed manually by the researcher. This process is tedious and prone to error and becomes a bottleneck in modern cancer research. Thus, an efficient, reliable, and rapid quantification method is in critical need. In this paper, we describe open contour-based tracking methods to automatically segment and track microtubule movements. We redefine the internal energy terms specifically for open snake, and examine different external energy terms for locating the end points of a microtubule. This algorithm has been validated using simulated images, untreated MCF-7 breast cancer cell lines, and cells treated with the microtubule-targeting chemotherapeutic agent, Taxol.