Gotowe bibliografie tematyczne / TeraVoxel

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  1. Artykuły w czasopismach
  2. Części książek
  3. Streszczenia konferencji

Gotowa bibliografia na temat „TeraVoxel”

Autor: Grafiati
Data publikacji: 3 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "TeraVoxel"

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Miettinen, Arttu, Ioannis Vogiatzis Oikonomidis, Anne Bonnin, and Marco Stampanoni. "NRStitcher: non-rigid stitching of terapixel-scale volumetric images." Bioinformatics 35, no. 24 (2019): 5290–97. http://dx.doi.org/10.1093/bioinformatics/btz423.

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Abstract Summary In modern microscopy, the field of view is often increased by obtaining an image mosaic, where multiple sub-images are taken side-by-side and combined post-acquisition. Mosaic imaging often leads to long imaging times that can increase the probability of sample deformation during the acquisition due to, e.g. changes in the environment, damage caused by the radiation used to probe the sample or biologically induced deterioration. Here we propose a technique, based on local phase correlation, to detect the deformations and construct an artifact-free image mosaic from deformed su
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Tondravi, Mehdi, William Scullin, Ming Du, et al. "A Pipeline for Distributed Segmentation of Teravoxel Tomography Datasets." Microscopy and Microanalysis 24, S2 (2018): 166–67. http://dx.doi.org/10.1017/s143192761801320x.

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Vescovi, Rafael, Ming Du, Vincent de Andrade, William Scullin, Dogˇa Gürsoy, and Chris Jacobsen. "Tomosaic: efficient acquisition and reconstruction of teravoxel tomography data using limited-size synchrotron X-ray beams." Journal of Synchrotron Radiation 25, no. 5 (2018): 1478–89. http://dx.doi.org/10.1107/s1600577518010093.

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X-rays offer high penetration with the potential for tomography of centimetre-sized specimens, but synchrotron beamlines often provide illumination that is only millimetres wide. Here an approach is demonstrated termed Tomosaic for tomographic imaging of large samples that extend beyond the illumination field of view of an X-ray imaging system. This includes software modules for image stitching and calibration, while making use of existing modules available in other packages for alignment and reconstruction. The approach is compatible with conventional beamline hardware, while providing a dose
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Shruthishree, S.H, and Tiwari Harshvardhan. "A REVIEW PAPER ON MEDICAL IMAGE PROCESSING." International Journal of Research - Granthaalayah 5, no. 4 RACSIT (2017): 21–29. https://doi.org/10.5281/zenodo.572290.

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Biomedical image processing has experienced dramatic expansion, and has been an interdisciplinary research field attracting expertise from applied mathematics, computer sciences, engineering, statistics, physics, biology and medicine. Computer-aided diagnostic processing has already become an important part of clinical routine. Accompanied by a rush of new development of high technology and use of various imaging modalities, more challenges arise; for example, how to process and analyze a significant volume of images so that high quality information can be produced for disease diagnoses and tr
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Bria, Alessandro, and Giulio Iannello. "TeraStitcher - A tool for fast automatic 3D-stitching of teravoxel-sized microscopy images." BMC Bioinformatics 13, no. 1 (2012). http://dx.doi.org/10.1186/1471-2105-13-316.

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Ruan, Xiongtao, Matthew Mueller, Gaoxiang Liu, et al. "Image processing tools for petabyte-scale light sheet microscopy data." Nature Methods, October 17, 2024. http://dx.doi.org/10.1038/s41592-024-02475-4.

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AbstractLight sheet microscopy is a powerful technique for high-speed three-dimensional imaging of subcellular dynamics and large biological specimens. However, it often generates datasets ranging from hundreds of gigabytes to petabytes in size for a single experiment. Conventional computational tools process such images far slower than the time to acquire them and often fail outright due to memory limitations. To address these challenges, we present PetaKit5D, a scalable software solution for efficient petabyte-scale light sheet image processing. This software incorporates a suite of commonly
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Li, Yuxin, Anan Li, Junhuai Li, et al. "webTDat: A Web-Based, Real-Time, 3D Visualization Framework for Mesoscopic Whole-Brain Images." Frontiers in Neuroinformatics 14 (January 13, 2021). http://dx.doi.org/10.3389/fninf.2020.542169.

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The popularity of mesoscopic whole-brain imaging techniques has increased dramatically, but these techniques generate teravoxel-sized volumetric image data. Visualizing or interacting with these massive data is both necessary and essential in the bioimage analysis pipeline; however, due to their size, researchers have difficulty using typical computers to process them. The existing solutions do not consider applying web visualization and three-dimensional (3D) volume rendering methods simultaneously to reduce the number of data copy operations and provide a better way to visualize 3D structure
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Attarpour, Ahmadreza, Jonas Osmann, Anthony Rinaldi, et al. "A deep learning pipeline for three-dimensional brain-wide mapping of local neuronal ensembles in teravoxel light-sheet microscopy." Nature Methods, January 27, 2025. https://doi.org/10.1038/s41592-024-02583-1.

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Humbel, Mattia, Christine Tanner, Marta Girona Alarcón, et al. "Synchrotron Radiation‐Based Tomography of an Entire Mouse Brain with Sub‐Micron Voxels: Augmenting Interactive Brain Atlases with Terabyte Data." Advanced Science, April 29, 2025. https://doi.org/10.1002/advs.202416879.

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AbstractSynchrotron radiation‐based X‐ray microtomography is uniquely suited for post‐mortem 3D visualization of organs such as the mouse brain. Tomographic imaging of the entire mouse brain with isotropic cellular resolution requires an extended field‐of‐view and produces datasets of multiple terabytes in size. These data must be reconstructed, analyzed, and made accessible to domain experts who may have limited image processing knowledge. Extended‐field X‐ray microtomography is presented with voxel size covering an entire mouse brain. The 4495 projections from 8 × 8 offset acquisitions are s
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Części książek na temat "TeraVoxel"

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Ashwini, Akanksha, and Jaerock Kwon. "Image Processing Pipeline for Web-Based Real-Time 3D Visualization of Teravoxel Volumes." In Data Mining and Big Data. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93803-5_19.

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Ding, Liya, Yabo Li, Jiayi Ding, Xiaoli Qi, Hu Zhao, and Hanchuan Peng. "Image Stitching of Teravoxel-Sized Whole-Brain Microscopy Data." In Series on Language Processing, Pattern Recognition, and Intelligent Systems. WORLD SCIENTIFIC, 2024. http://dx.doi.org/10.1142/9789811286131_0010.

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Streszczenia konferencji na temat "TeraVoxel"

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Raghavan, Shruthi, and Jaerock Kwon. "Tracing Tubular Structures from Teravoxel-Sized Microscope Images." In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018. http://dx.doi.org/10.1109/embc.2018.8512288.

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Engel, Klaus. "CERA-TVR: A framework for interactive high-quality teravoxel volume visualization on standard PCs." In 2011 IEEE Symposium on Large Data Analysis and Visualization (LDAV). IEEE, 2011. http://dx.doi.org/10.1109/ldav.2011.6092330.

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