Spatio-temporal Diffusion-based Dynamic Cell segmentation

Cell segmentation is a critical step for quantification and monitoring of cell behavior in image sequences. In this study, we propose to use a non-linear heat diffusion equation model in the joint spatio-temporal domain for cell segmentation of time-lapse image sequences. Moving regions are initially detected in each set of three consecutive sequence images by numerically solving a system of coupled spatio-temporal partial differential equations and determining the optimal values for the temporal and spatial diffusion parameters. After the spatio-temporal diffusion stage is completed, we compute the edge map by non-parametric density estimation using Parzen kernels. This process is followed by watershed-based segmentation to detect the moving cells. We applied this method on several datasets of fluorescence microscopy images with varying levels of difficulty with respect to cell density, resolution, contrast, and signal-to-noise ratio. We compared the results with those produced by Chan and Vese level-set based segmentation and a temporally linked level set technique. We validated all segmentation techniques against reference masks provided by the international Cell Tracking Challenge consortium. Our proposed method produced encouraging segmentation accuracy, especially when applied to images containing cells undergoing mitosis and low SNR. The performance evaluation clearly indicates the efficiency and robustness of this method in detecting and segmenting the cells with an average Dice similarity coefficient of 85% over a variety of simulated and real fluorescent image sequences. The proposed technique yielded average improvements of 7% in segmentation accuracy compared to both strictly spatial and temporally linked Chan-Vese techniques.