A Dynamic-Shape-Prior Guided Snake Model With Application in Visually Tracking Dense Cell Populations


Here, we present the DSP snake model, which we believe will help improve the overall stability of the point-based snake model by using dynamic shape-priority (DSP). At its core is an efficient unification of a variety of high-level priors into one new force term for snakes. First, a global-topology regularity is presented to solve the intrinsic self-intersection problem for snakes. The uneven distribution of a snake's snaxels over the contour is also taken care of, resulting in a satisfactory parameterization. This method is different from other methods that use learning templates or enforce hard priors, which strongly respects the model's deformation flexibility while maintaining a reasonable global topology for the snake. This technique may effectively prevent snakes from self-crossing or automatically untie an already self-intersecting shape, as demonstrated in testing. This model is also integrated with existing forces and applied to the extremely difficult task of tracking dense living cell populations. The DSP G-snake model has improved tracking accuracy by up to 30% compared to traditional model-based techniques.. Real-world cell datasets with dense populations and substantial displacements were used to demonstrate that the suggested approach outperforms modern active-contour competitors and current cell tracking frameworks in terms of accuracy and speed.

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