An object splitting model using higher-order active contours for single-cell segmentation

Abstract

Determining the number and morphology of individual cells on microscopy images is one of the most fundamental steps in quantitative biological image analysis. Cultured cells used in genetic perturbation and drug discovery experiments can pile up and nuclei can touch or even grow on top of each other. Similarly, in tissue sections cell nuclei can be very close and touch each other as well. This makes single cell nuclei detection extremely challenging using current segmentation methods, such as classical edge- and threshold-based methods that can only detect separate objects, and they fail to separate touching ones. The pipeline we present here can segment individual cell nuclei by splitting touching ones. The two-step approach merely based on energy minimization principles using an active contour framework. In a presegmentation phase we use a local region data term with strong edge tracking capability, while in the splitting phase we introduce a higher-order active contour model. This model prefers high curvature contour locations at the opposite side of joint objects grow “cutting arms” that evolve to one another until they split objects. Synthetic and real experiments show the strong segmentation and splitting ability of the proposed pipeline and that it outperforms currently used segmentation models.