Abstract
Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. Successful cell segmentation algorithms rely identifying seed points, and are highly sensitive to variablility in cell size. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional contour evolution that extends previous work on fast two-dimensional snakes. 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 localization tasks when compared to existing methods on large 3D brain images.
Highlights
Quantifying the size and distribution of cell nuclei in optical images is critical to understanding the underlying tissue structure [1] and organization [2, 3]
Transgenic mice that model Dravet syndrome with spontaneous seizure onset at postnatal day 15 were housed in a 12 hour light/dark cycle
Since any 3D contour is assigned to one graphic processing units (GPUs) thread, the number of utilized threads is equal to the number of initial 3D snakes
Summary
Quantifying the size and distribution of cell nuclei in optical images is critical to understanding the underlying tissue structure [1] and organization [2, 3]. Since manual analysis of microscopy images is time consuming and labor intensive, automated cell localization is essential for detecting and segmenting cells in massive images. Most current algorithms use basic techniques combined with complicated pipelines to overcome those challenges. These methods include thresholding [7,8,9], feature extraction [10, 11], classification [12], c-means [8] and k-means [13] clustering, region growing [14,15,16], and deformable models [17,18,19]
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