Fast and robust optical flow for time-lapse microscopy using super-voxels

Research Area: A.2-Light Microscopy Year: 2013
Type of Publication: Article Keywords: optical flow, light-sheet microscopy, super-voxels, markov random field
Publication: Bioinformatics
Motivation: Optical flow is a key method used for quantitative motion estimation of biological structures in light microscopy. It has also been used as a key module in segmentation and tracking systems and is considered a mature technology in the field of computer vision. Howe- ver, most of the research focused on two-dimensional natural images, which are small in size and rich in edges and texture information. In contrast, three-dimensional (3D) time-lapse recordings of biological specimens comprise up to several terabytes of image data and often exhibit complex object dynamics as well as blurring due to the point- spread-function of the microscope. Thus, new approaches to optical flow are required to improve performance for such data. Results: We solve optical flow in large 3D+time microscopy data- sets 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 spe- cific characteristics of light microscopy datasets: super-voxels help registration in textureless areas, the MRF over super-voxels efficien- tly 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 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.