2015
DOI: 10.1186/1471-2105-16-s11-s8
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Physically-based in silico light sheet microscopy for visualizing fluorescent brain models

Abstract: BackgroundWe present a physically-based computational model of the light sheet fluorescence microscope (LSFM). Based on Monte Carlo ray tracing and geometric optics, our method simulates the operational aspects and image formation process of the LSFM. This simulated, in silico LSFM creates synthetic images of digital fluorescent specimens that can resemble those generated by a real LSFM, as opposed to established visualization methods producing visually-plausible images. We also propose an accurate fluorescenc… Show more

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Cited by 13 publications
(17 citation statements)
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References 43 publications
(50 reference statements)
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“…The resolution of the largest dimension of each volume is set to 8000 voxels. The area covered by the orange box in ( e ) represents the maximum volumetric extent that could be simulated in similar previous studies [ 28 , 29 ] …”
Section: Resultsmentioning
confidence: 84%
See 2 more Smart Citations
“…The resolution of the largest dimension of each volume is set to 8000 voxels. The area covered by the orange box in ( e ) represents the maximum volumetric extent that could be simulated in similar previous studies [ 28 , 29 ] …”
Section: Resultsmentioning
confidence: 84%
“…Note that we only voxelize a fraction of neurons to be able to visualize the volume, but in principle the volumes were created for all the neurons composing the circuit. Referring to previous studies [ 28 , 29 ], the scalability concerns addressed in this work has allowed the computational neuroscientists to extend the scale of their simulations from the size of the box colored in orange in Fig. 6 to an entire slice.…”
Section: Resultsmentioning
confidence: 97%
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“…al. on the development of a computational workflow for in-silico analysis of LSM for brain models [42]. The simulated images of this study were used to generate plots of image contrast (SBR) as a function of optical length, which highlights the achievable imaging depth in tissue for each microscope geometry.…”
Section: Discussionmentioning
confidence: 99%
“…Finally, three papers are included from the imaging field. Topics range from image generation, as discussed by Abdellah et al [ 7 ], to a method for parameter optimization in image processing by Pretorius et al [ 9 ] (e.g. for cell nuclei detection and colour deconvolution for histology), and all the way to graph-based exploration of histology images in the GRAPHIE system proposed by Ding et al [ 8 ].…”
mentioning
confidence: 99%