Spatio-temporal level-set based cell segmentation in time-lapse image sequences

Abstract

Automated segmentation and tracking of cells in time-lapse imaging is a process of fundamental significance in several biomedical applications. In this work our interest is focused on cell segmentation over a set of fluorescence microscopy images with varying levels of difficulty with respect to cell density, resolution, contrast, and signal-to-noise ratio. We utilize a region-based approach to curve evolution based on the level-set formulation. We introduce and test the use of temporal linking for level-set initialization to improve the robustness and computational time of level-set convergence. We validate our segmentation approach against manually segmented images provided by the Cell Tracking Challenge consortium. Our method produces encouraging segmentation results with an average DICE score of 0.78 over a variety of simulated and real sequences and speeds up the convergence rate by an average factor of 10.2.