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Efficient processing and analysis of large-scale light-sheet microscopy data

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Efficient processing and analysis of large-scale light-sheet microscopy data
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Light-sheet microscopy is a powerful method for imaging the development and function of complex biological systems at high spatiotemporal resolution and over long time scales. Such experiments typically generate terabytes of multidimensional image data, and thus they demand efficient computational solutions for data management, processing and analysis. We present protocols and software to tackle these steps, focusing on the imaging-based study of animal development. Our protocols facilitate (i) high-speed lossless data compression and content-based multiview image fusion optimized for multicore CPU architectures, reducing image data size 30–500-fold; (ii) automated large-scale cell tracking and segmentation; and (iii) visualization, editing and annotation of multiterabyte image data and cell-lineage reconstructions with tens of millions of data points. These software modules are open source. They provide high data throughput using a single computer workstation and are readily applicable to a wide spectrum of biological model systems.

The publication of the light-sheet microscopy image processing and analysis framework is available from the literature section (Amat, Höckendorf, Wan, Lemon, McDole and Keller 2015, Nature Protocols).

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Related Links

  • Download Supplementary Software
    Supplementary Software 1: KLB lossless compression file format
    Supplementary Software 2: KLB Java Native Interface library and SCIFIO implementation
    Supplementary Software 3: Image processing pipeline for light-sheet microscopy
    Supplementary Software 4: TGMM software for segmentation and cell tracking
    Supplementary Software 5: CATMAID branch for 5D image visualization and lineage editing
    Supplementary Software 6: Matlab import/export scripts for TGMM, CATMAID and Imaris
  • Download Supplementary Data
    Supplementary Data 1: Example data for image masking and KLB image compression
    Supplementary Data 2: Example data for multi-view image registration and fusion