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Fast segmentation from blurred data in 3D fluorescence microscopy.
IEEE Trans. Image Process. 26, 4856-4870 (2017)
OAPA We develop a fast algorithm for segmenting 3D images from linear measurements based on the Potts model (or piecewise constant Mumford-Shah model). To that end, we first derive suitable space discretizations of the 3D Potts model which are capable of dealing with 3D images defined on non-cubic grids. Our discretization allows us to utilize a specific splitting approach which results in decoupled subproblems of moderate size. The crucial point in the 3D setup is that the number of independent subproblems is so large that we can reasonably exploit the parallel processing capabilities of the graphics processing units (GPU). Our GPU implementation is up to 18 times faster than the sequential CPU version. This allows to process even large volumes in acceptable runtimes. As a further contribution, we extend the algorithm in order to deal with non-negativity constraints. We demonstrate the efficiency of our method for combined image deconvolution and segmentation on simulated data and on real 3D widefield fluorescence microscopy data.
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Publication type Article: Journal article
Document type Scientific Article
Keywords 3d Images ; Gpu ; Graphics Processing Units ; Image Reconstruction ; Image Segmentation ; Image Segmentation ; Microscopy ; Non-negativity Constraints ; Parallelization ; Piecewise Constant Mumford-shah Model ; Potts Model ; Solid Modeling ; Three-dimensional Displays; Electrical-impedance Tomography; Level-set Approach; Image Segmentation; Minimal Partitions; Active Contours; Graph Cuts; Mumford; Regularization; Deconvolution; Restoration
Institute(s) Institute of Computational Biology (ICB)