We solve optical flow in large 3D time-lapse microscopy datasets by defining a Markov random field (MRF) over super-voxels in the foreground and applying motion smoothness constraints between super-voxels instead of voxel-wise. This model is tailored to the specific characteristics of light microscopy datasets: super-voxels help registration in textureless areas, the MRF over super-voxels efficiently propagates motion information between neighboring cells, and the background subtraction and super-voxels reduce the dimensionality of the problem by an order of magnitude. We validate our approach on large 3D time-lapse datasets of Drosophila and zebrafish development by analyzing cell motion patterns. We show that our approach is, on average, 10x faster than commonly used optical flow implementations in the Insight Tool-Kit (ITK) and reduces the average flow endpoint error by 50% in regions with complex dynamic processes, such as cell divisions.
The publication of the optical flow algorithm is available in the literature section (Amat, Myers and Keller 2013, Bioinformatics).
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