Three-Dimensional GPU-Accelerated Active Contours for Automated Localization of Cells in Large Images
This work addresses the challenge of automated cell localization in large-scale microscopy for researchers in biomedical imaging, though it is incremental as it extends prior 2D active contour techniques to 3D.
The paper tackles the problem of cell segmentation in large microscopy images by developing an efficient, GPU-accelerated 3D active contour method, which shows superior performance in segmenting cells in large 2D and 3D brain images compared to existing methods.
Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. This becomes particularly challenging for extremely large images, since manual intervention and processing time can make segmentation intractable. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional (3D) contour evolution that extends previous work on fast two-dimensional active contours. We provide a formulation for optimization on 3D images, as well as a strategy for accelerating computation on consumer graphics hardware. The proposed software takes advantage of Monte-Carlo sampling schemes in order to speed up convergence and reduce thread divergence. Experimental results show that this method provides superior performance for large 2D and 3D cell segmentation tasks when compared to existing methods on large 3D brain images.