ABSTRACT 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,

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PETABYTE-SCALE LIGHT SHEET MICROSCOPY DATA Article Open access 17 October 2024 HIGH-THROUGHPUT IMAGE PROCESSING SOFTWARE FOR THE STUDY OF NUCLEAR ARCHITECTURE AND GENE EXPRESSION Article

Open access 08 August 2024 VITESSCE: INTEGRATIVE VISUALIZATION OF MULTIMODAL AND SPATIALLY RESOLVED SINGLE-CELL DATA Article Open access 27 September 2024 REFERENCES * Voie, A.H., Burns,

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Central  Google Scholar  Download references ACKNOWLEDGEMENTS We thank A. Cardona and the participants of the Janelia CATMAID hackathon for help with modifying the open-source code of

CATMAID; K. Khairy for his contributions to exploring approaches to multiview image fusion and SiMView data management; and K. Branson and A. Cardona for helpful comments on the manuscript.

This work was supported by the Howard Hughes Medical Institute. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, Virginia, USA

Fernando Amat, Burkhard Höckendorf, Yinan Wan, William C Lemon, Katie McDole & Philipp J Keller Authors * Fernando Amat View author publications You can also search for this author

inPubMed Google Scholar * Burkhard Höckendorf View author publications You can also search for this author inPubMed Google Scholar * Yinan Wan View author publications You can also search

for this author inPubMed Google Scholar * William C Lemon View author publications You can also search for this author inPubMed Google Scholar * Katie McDole View author publications You can

also search for this author inPubMed Google Scholar * Philipp J Keller View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS F.A. and B.H.

developed the KLB file format and related software infrastructure. P.J.K. developed the multiview registration and fusion software, with contributions from F.A. F.A. developed the TGMM

framework and related software infrastructure. Y.W., W.C.L. and K.M. performed light-sheet microscopy experiments and contributed image data sets. F.A. and P.J.K. wrote the manuscript, with

input from all authors. CORRESPONDING AUTHORS Correspondence to Fernando Amat or Philipp J Keller. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing financial

interests. INTEGRATED SUPPLEMENTARY INFORMATION SUPPLEMENTARY FIGURE 1 LOCAL PERFORMANCE OF LOSSLESS COMPRESSION IMAGE FILE FORMATS Performance of the KLB lossless compression format vs.

LZW-TIFF (green) and JPEG 2000 (blue) lossless compression formats with respect to write speed (first column) and read speed (second column). The JPEG 2000 benchmark utilizes the

multi-threaded commercial library PICTools Medical SDK (Accusoft). A performance comparison of KLB and uncompressed TIFF formats is included as well (orange). LZW-TIFF and uncompressed TIFF

benchmarks utilize the _imread_ and _imwrite_ functions provided by the Image Processing Toolbox in Matlab. All performance data are provided as ratios with KLB performance in the numerator,

i.e. ratios larger than one (grey lines) indicate superior performance of the KLB format. The comparison was performed using a variety of fluorescence microscopy image data sets stored

locally on a high-performance RAID array built from solid-state drives (SSDs) and thus complements the network-based analysis shown in Fig. 3 (note that performance with respect to

compression ratios is identical to the data shown in Fig. 3). Benchmark data sets include SiMView light-sheet microscopy recordings of fruit fly, mouse and zebrafish embryonic development

(data sets 1-8), confocal microscopy data of a zebrafish embryo (data set 9) and SiMView functional image data of brain activity in a larval zebrafish (data set 10). Developmental data sets

(data sets 1-8) were analyzed as raw and masked versions in order to illustrate the importance of background masking for maximizing data storage and access efficiency. Please see Steps I-III

in Fig. 2 for a description of the concepts underlying background masking. SUPPLEMENTARY FIGURE 2 BLOCK-SIZE DEPENDENCY OF KLB FILE SIZE AND READ/WRITE SPEEDS Performance comparison for KLB

versus JPEG 2000 (JP2) with respect to file size (a), write time (b) and read time (c), as a function of KLB block size (in pixels). The results represent average performance across five

data sets, including developmental image data from a fruit fly embryo, a zebrafish embryo and early-/late-stage mouse embryos as well as functional image data from a zebrafish larva. The

larger the block size, the better the KLB compression ratio; however, this ratio reaches saturation already for relatively small block sizes. Read and write times are not optimal for extreme

block sizes, i.e. both for very small and for very large blocks. If blocks are too small, communication overhead in processing threads becomes an issue. If blocks are too large,

computations cannot be parallelized to the maximum extent (in the most extreme scenario, a single thread has to handle the entire image). The figure shows a diagonal band, where all three

metrics are optimal or near optimal at the same time. Based on these benchmarks, we chose the default block size as 96 x 96 x 8 pixels. The JPEG 2000 benchmark utilizes the multi-threaded

commercial library PICTools Medical SDK (Accusoft). Lateral size refers to the X and Y axes of the image volume. Axial size refers to the Z axis of the image volume, which is typically

smaller than the lateral size in light microscopy due to anisotropic spatial resolution in the microscope and anisotropic spatial sampling of the specimen volume. SUPPLEMENTARY FIGURE 3 KLB

PERFORMANCE COMPARISON FOR LOCAL VS. NETWORK DATA STORAGE Comparison of KLB read and write speeds on a local data drive versus a data drive mounted over the network (using a 10 Gb/s glass

fiber connection). Speeds are comparable since most of the time is spent on data compression and decompression, respectively, and physical disk access introduces relatively little overhead.

Moreover, most modern operating systems and RAID hardware improve I/O performance by caching and by using dedicated processors that avoid load on primary CPUs. Thus, while some blocks are

compressed or decompressed others are written or read, respectively, masking I/O costs. All data points are averages based on _n_ = 5 iterations of the benchmark. SUPPLEMENTARY INFORMATION

SUPPLEMENTARY TEXT AND FIGURES Supplementary Figures 1–3, Supplementary Note 1, Supplementary Table 1 (PDF 1261 kb) SUPPLEMENTARY SOFTWARE 1 KLB lossless compression file format. This

software package contains the C++11 source code for the KLB file format implementation as well as wrappers for Matlab and Java. The folder _bin_ contains the precompiled static and shared

(DLL) libraries for Windows 7 64-bit as well as a simple executable _test_KLBIO.exe_ for testing read/write operations. The source code of this executable represents a good example of how to

use the API for the KLB file format. For Windows 7 64-bit, we also provide precompiled MEX files in the folder _matlabWrapper_. Linux and Mac OS users need to compile both the source code

and the Matlab wrappers to obtain libraries and executables. For the first part, a CMake file is available in the folder _src_. For the second part, the folder _matlabWrapper_ contains the

script _compileMex.m_ for generating MEX files. The C++ libraries need to be compiled in release mode before compiling the MEX files. In order to keep track of possible software updates, the

user can also clone all files from the primary public software repository using the following git command: _git clone

_https://[email protected]/fernandoamat/keller-lab-block-filetype.git (ZIP 4460 kb) SUPPLEMENTARY SOFTWARE 2 KLB Java Native Interface library and SCIFIO implementation. This

software package exposes the C++ API on the Java side and includes a functional implementation of a SCIFIO format that provides KLB support to image processing frameworks such as ImageJ and

Knime. Precompiled native libraries for Windows and Linux (64-bit) are bundled inside the JAR file included in this software package. For convenience, ImageJ users can follow the update site

at http://sites.imagej.net/SiMView (for instructions, see http://wiki.imagej.net/How_to_follow_a_3rd_party_update_site). (ZIP 1099 kb) SUPPLEMENTARY SOFTWARE 3 Image processing pipeline for

light-sheet microscopy. This software package contains our Matlab code for image processing of light-sheet microscopy data sets, including (1) sCMOS image correction, background masking and

KLB lossless image compression (using script _clusterPT.m_), (2) content-based multi-view image registration and fusion (using scripts _clusterMF.m_, localAP.m and _clusterTF.m_), (3)

spatial drift correction and intensity normalization (using scripts _localEC.m_ and _clusterCS.m_) and (4) adaptive local background correction (using script _clusterFR.m_). Please see the

README file for detailed information about these software modules. (ZIP 1003 kb) SUPPLEMENTARY SOFTWARE 4 TGMM software for segmentation and cell tracking. This software package contains the

C++ and CUDA source code for the Tracking with Gaussian Mixture Models (TGMM) software for automated segmentation and cell tracking in light microscopy time-lapse data sets. The software

package includes the following folders: _src_: contains all source code files. This folder also includes the file _CMakeList.txt_ that can be used to compile the source code. _doc_: contains

the documentation of the TGMM software. _bin_: contains Windows 7 64bit executables for running the TGMM software. When compiling the source code, the executables for the release version

will be placed here. This folder also contains all necessary DLLs (CUDA and MSVC runtime) as well as the text files containing machine learning classifiers for cell division detection.

Please see the README file for detailed information on how to run and compile the TGMM software. In order to keep track of possible software updates, the user can also clone all files from

the primary public software repository using the following git command: _git clone _git://git.code.sf.net/p/tgmm/code tgmm-code (ZIP 72857 kb) SUPPLEMENTARY SOFTWARE 5 CATMAID branch for 5D

image visualization and lineage editing. This software package contains our branch of the open source software CATMAID. The software can also be cloned using the following git command: _git

clone -b 5Dvisualization --single-branch _https://[email protected]/fernandoamat/catmaid_5d_visualization_annotation.git The PDF file UserGuide.pdf in the root folder of this

software package and the website http://catmaid.org/ provide detailed instructions for setting up a CATMAID server. (ZIP 19979 kb) SUPPLEMENTARY SOFTWARE 6 Matlab import/export scripts for

TGMM, CATMAID and Imaris. This software package contains Matlab code for transferring results between TGMM, CATMAID and Imaris. In order to optimize read speed, the code for reading XML

files generated by TGMM needs to be compiled into MEX files. The folder _readTGMM_XMLoutput contains the script compileMex.m_ for this purpose. The README file contains further details on

this topic and a description of the main Matlab functions included in this software package. Briefly, these Matlab functions facilitate: (1) import of TGMM tracking and segmentation results

into Matlab, (2) export of image data and tracking results from Matlab to CATMAID, (3) import of cell lineage information from CATMAID into Matlab, (4) export of cell lineage information

from Matlab to Imaris. (ZIP 3470 kb) RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Amat, F., Höckendorf, B., Wan, Y. _et al._ Efficient processing and

analysis of large-scale light-sheet microscopy data. _Nat Protoc_ 10, 1679–1696 (2015). https://doi.org/10.1038/nprot.2015.111 Download citation * Published: 01 October 2015 * Issue Date:

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