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Cell segmentation in microscopy is a challenging problem, since cells areoften asymmetric and densely packed. This becomes particularly challenging forextremely large images, since manual intervention and processing time can makesegmentation intractable. In this paper, we present an efficient and highlyparallel formulation for symmetric three-dimensional (3D) contour evolutionthat extends previous work on fast two-dimensional active contours. We providea formulation for optimization on 3D images, as well as a strategy foraccelerating computation on consumer graphics hardware. The proposed softwaretakes advantage of Monte-Carlo sampling schemes in order to speed upconvergence and reduce thread divergence. Experimental results show that thismethod provides superior performance for large 2D and 3D cell segmentationtasks when compared to existing methods on large 3D brain images.

Three-Dimensional GPU-Accelerated Active Contours for Automated Localization of Cells in Large Images