Hydra image processor: 5-D GPU image analysis library with MATLAB and python wrappers
Light microscopes can now capture data in five dimensions at very high frame rates producing terabytes of data per experiment. Five-dimensional data has three spatial dimensions (x, y, z), multiple channels (λ) and time (t). Current tools are prohibitively time consuming and do not efficiently utili...
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| Published in | Bioinformatics (Oxford, England) Vol. 35; no. 24; pp. 5393 - 5395 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Oxford University Press
15.12.2019
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1367-4803 1367-4811 1367-4811 |
| DOI | 10.1093/bioinformatics/btz523 |
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| Abstract | Light microscopes can now capture data in five dimensions at very high frame rates producing terabytes of data per experiment. Five-dimensional data has three spatial dimensions (x, y, z), multiple channels (λ) and time (t). Current tools are prohibitively time consuming and do not efficiently utilize available hardware. The hydra image processor (HIP) is a new library providing hardware-accelerated image processing accessible from interpreted languages including MATLAB and Python. HIP automatically distributes data/computation across system and video RAM allowing hardware-accelerated processing of arbitrarily large images. HIP also partitions compute tasks optimally across multiple GPUs. HIP includes a new kernel renormalization reducing boundary effects associated with widely used padding approaches.
HIP is free and open source software released under the BSD 3-Clause License. Source code and compiled binary files will be maintained on http://www.hydraimageprocessor.com. A comprehensive description of all MATLAB and Python interfaces and user documents are provided. HIP includes GPU-accelerated support for most common image processing operations in 2-D and 3-D and is easily extensible. HIP uses the NVIDIA CUDA interface to access the GPU. CUDA is well supported on Windows and Linux with macOS support in the future. |
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| AbstractList | Light microscopes can now capture data in five dimensions at very high frame rates producing terabytes of data per experiment. Five-dimensional data has three spatial dimensions (x, y, z), multiple channels (λ) and time (t). Current tools are prohibitively time consuming and do not efficiently utilize available hardware. The hydra image processor (HIP) is a new library providing hardware-accelerated image processing accessible from interpreted languages including MATLAB and Python. HIP automatically distributes data/computation across system and video RAM allowing hardware-accelerated processing of arbitrarily large images. HIP also partitions compute tasks optimally across multiple GPUs. HIP includes a new kernel renormalization reducing boundary effects associated with widely used padding approaches.SUMMARYLight microscopes can now capture data in five dimensions at very high frame rates producing terabytes of data per experiment. Five-dimensional data has three spatial dimensions (x, y, z), multiple channels (λ) and time (t). Current tools are prohibitively time consuming and do not efficiently utilize available hardware. The hydra image processor (HIP) is a new library providing hardware-accelerated image processing accessible from interpreted languages including MATLAB and Python. HIP automatically distributes data/computation across system and video RAM allowing hardware-accelerated processing of arbitrarily large images. HIP also partitions compute tasks optimally across multiple GPUs. HIP includes a new kernel renormalization reducing boundary effects associated with widely used padding approaches.HIP is free and open source software released under the BSD 3-Clause License. Source code and compiled binary files will be maintained on http://www.hydraimageprocessor.com. A comprehensive description of all MATLAB and Python interfaces and user documents are provided. HIP includes GPU-accelerated support for most common image processing operations in 2-D and 3-D and is easily extensible. HIP uses the NVIDIA CUDA interface to access the GPU. CUDA is well supported on Windows and Linux with macOS support in the future.AVAILABILITY AND IMPLEMENTATIONHIP is free and open source software released under the BSD 3-Clause License. Source code and compiled binary files will be maintained on http://www.hydraimageprocessor.com. A comprehensive description of all MATLAB and Python interfaces and user documents are provided. HIP includes GPU-accelerated support for most common image processing operations in 2-D and 3-D and is easily extensible. HIP uses the NVIDIA CUDA interface to access the GPU. CUDA is well supported on Windows and Linux with macOS support in the future. Light microscopes can now capture data in five dimensions at very high frame rates producing terabytes of data per experiment. Five-dimensional data has three spatial dimensions (x, y, z), multiple channels (λ) and time (t). Current tools are prohibitively time consuming and do not efficiently utilize available hardware. The hydra image processor (HIP) is a new library providing hardware-accelerated image processing accessible from interpreted languages including MATLAB and Python. HIP automatically distributes data/computation across system and video RAM allowing hardware-accelerated processing of arbitrarily large images. HIP also partitions compute tasks optimally across multiple GPUs. HIP includes a new kernel renormalization reducing boundary effects associated with widely used padding approaches. HIP is free and open source software released under the BSD 3-Clause License. Source code and compiled binary files will be maintained on http://www.hydraimageprocessor.com. A comprehensive description of all MATLAB and Python interfaces and user documents are provided. HIP includes GPU-accelerated support for most common image processing operations in 2-D and 3-D and is easily extensible. HIP uses the NVIDIA CUDA interface to access the GPU. CUDA is well supported on Windows and Linux with macOS support in the future. |
| Author | Winter, Mark Cohen, Andrew R Wait, Eric |
| AuthorAffiliation | Electrical and Computer Engineering, Drexel University , Philadelphia, PA, USA |
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| Cites_doi | 10.1038/nprot.2015.111 10.1016/j.media.2013.05.008 10.1083/jcb.201004104 10.1093/bioinformatics/btw406 10.1186/1471-2105-15-328 10.1016/j.stemcr.2015.08.002 10.1038/nmeth.3372 |
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| Copyright | The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com 2019 |
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| References | Royer (2023013108412521100_btz523-B4) 2015; 6 Linkert (2023013108412521100_btz523-B3) 2010; 5 Winter (2023013108412521100_btz523-B6) 2016; 32 Winter (2023013108412521100_btz523-B7) 2015; 5 Wait (2023013108412521100_btz523-B5) 2014; 15 Amat (2023013108412521100_btz523-B1) 2015; 11 Eklund (2023013108412521100_btz523-B2) 2013; 17 |
| References_xml | – volume: 11 start-page: 1679 year: 2015 ident: 2023013108412521100_btz523-B1 article-title: Efficient processing and analysis of large-scale light-sheet microscopy data publication-title: Nat. Protoc doi: 10.1038/nprot.2015.111 – volume: 17 start-page: 1073 year: 2013 ident: 2023013108412521100_btz523-B2 article-title: Medical image processing on the GPU—past, present and future publication-title: Med. Image Anal doi: 10.1016/j.media.2013.05.008 – volume: 5 start-page: 777 year: 2010 ident: 2023013108412521100_btz523-B3 article-title: Metadata matters: access to image data in the real world publication-title: J. Cell Biol doi: 10.1083/jcb.201004104 – volume: 32 start-page: 3530 year: 2016 ident: 2023013108412521100_btz523-B6 article-title: LEVER: software tools for segmentation, tracking and lineaging of proliferating cells publication-title: Bioinformatics doi: 10.1093/bioinformatics/btw406 – volume: 15 start-page: 328. year: 2014 ident: 2023013108412521100_btz523-B5 article-title: Visualization and correction of automated segmentation, tracking and lineaging from 5-D stem cell image sequences publication-title: BMC Bioinform doi: 10.1186/1471-2105-15-328 – volume: 5 start-page: 609 year: 2015 ident: 2023013108412521100_btz523-B7 article-title: Computational image analysis reveals intrinsic multigenerational differences between anterior and posterior cerebral cortex neural progenitor cells publication-title: Stem Cell Reports doi: 10.1016/j.stemcr.2015.08.002 – volume: 6 start-page: 480 year: 2015 ident: 2023013108412521100_btz523-B4 article-title: ClearVolume: open-source live 3D visualization for light-sheet microscopy publication-title: Nat. Methods doi: 10.1038/nmeth.3372 |
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| Title | Hydra image processor: 5-D GPU image analysis library with MATLAB and python wrappers |
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