Image Reconstruction in Light-Sheet Microscopy: Spatially Varying Deconvolution and Mixed Noise

Toader, Bogdan; Boulanger, Jérôme; Korolev, Yury; Lenz, Martin; Manton, James D.; Schönlieb, Carola‐Bibiane; Mureşan, Leila

doi:10.1007/s10851-022-01100-3

Cited by 8 publications

(3 citation statements)

References 49 publications

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“…Thus, to resolve fine structures, one has to orient the correctly the embryo to benefit from the XY resolution provided by the ×4 and ×12 objectives. Future development of deconvolution might improve Z resolution [67][68][69].…”

Section: Discussionmentioning

confidence: 99%

3D exploration of gene expression in chicken embryos through combined RNA fluorescence in situ hybridization, immunofluorescence, and clearing

André,

Dinvaut,

Castellani

et al. 2024

BMC Biol

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Background Fine characterization of gene expression patterns is crucial to understand many aspects of embryonic development. The chicken embryo is a well-established and valuable animal model for developmental biology. The period spanning from the third to sixth embryonic days (E3 to E6) is critical for many organ developments. Hybridization chain reaction RNA fluorescent in situ hybridization (HCR RNA-FISH) enables multiplex RNA detection in thick samples including embryos of various animal models. However, its use is limited by tissue opacity. Results We optimized HCR RNA-FISH protocol to efficiently label RNAs in whole mount chicken embryos from E3.5 to E5.5 and adapted it to ethyl cinnamate (ECi) tissue clearing. We show that light sheet imaging of HCR RNA-FISH after ECi clearing allows RNA expression analysis within embryonic tissues with good sensitivity and spatial resolution. Finally, whole mount immunofluorescence can be performed after HCR RNA-FISH enabling as exemplified to assay complex spatial relationships between axons and their environment or to monitor GFP electroporated neurons. Conclusions We could extend the use of HCR RNA-FISH to older chick embryos by optimizing HCR RNA-FISH and combining it with tissue clearing and 3D imaging. The integration of immunostaining makes possible to combine gene expression with classical cell markers, to correlate expressions with morphological differentiation and to depict gene expressions in gain or loss of function contexts. Altogether, this combined procedure further extends the potential of HCR RNA-FISH technique for chicken embryology.

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Section: Discussionmentioning

confidence: 99%

3D exploration of gene expression in chicken embryos through combined RNA fluorescence in situ hybridization, immunofluorescence, and clearing

André,

Dinvaut,

Castellani

et al. 2024

BMC Biol

View full text Add to dashboard Cite

show abstract

“…Lastly, our algorithm assumes uniform PSF blurring thorough the entire imaged volume. Although this is often considered a good approximation, higher image quality can be reached when considering spatially anisoplanatic deconvolutions (Toader et al, 2021 ) or mixed optical aberration corrections (Furieri et al, 2022 ). These considerations fall beyond the scope of the present manuscript, which may open new paths for further studies.…”

Section: Discussionmentioning

confidence: 99%

Blind deconvolution in autocorrelation inversion for multiview light‐sheet microscopy

Corbetta

Candeo

Bassi

et al. 2022

Microscopy Res & Technique

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Combining the information coming from multiview acquisitions is a problem of great interest in light-sheet microscopy. Aligning the views and increasing the resolution of their fusion can be challenging, especially if the setup is not fully calibrated. Here, we tackle these issues by proposing a new reconstruction method based on autocorrelation inversion that avoids alignment procedures. On top of this, we add a blind deconvolution step to improve the resolution of the final reconstruction. Our method permits us to achieve inherently aligned, highly resolved reconstructions while, at the same time, estimating the unknown point-spread function of the system. Research Highlights• We tackle the problem of multiview light-sheet deconvolution with a blind approach of autocorrelation inversion • Our method recovers the object and PSF, requires no alignment and calibration, and enhances the reconstruction of the specimen.

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“…Another study explores the scenario of spatially varying deconvolution. In [12], an image-formation model was developed to simulate the physics of a light sheet microscope, incorporating both the objective Point Spread Function and the light sheet illumination. The optical aberrations within the pupil function of the objective PSF are fitted with a linear combination of Zernike polynomials and constrained through a least square regression of bead images.…”

Section: Related Workmentioning

confidence: 99%

Distortion Correction and Denoising of Light Sheet Fluorescence Images

Julia,

Iguernaissi,

Michel

et al. 2024

Sensors

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Light Sheet Fluorescence Microscopy (LSFM) has emerged as a valuable tool for neurobiologists, enabling the rapid and high-quality volumetric imaging of mice brains. However, inherent artifacts and distortions introduced during the imaging process necessitate careful enhancement of LSFM images for optimal 3D reconstructions. This work aims to correct images slice by slice before reconstructing 3D volumes. Our approach involves a three-step process: firstly, the implementation of a deblurring algorithm using the work of K. Becker; secondly, an automatic contrast enhancement; and thirdly, the development of a convolutional denoising auto-encoder featuring skip connections to effectively address noise introduced by contrast enhancement, particularly excelling in handling mixed Poisson–Gaussian noise. Additionally, we tackle the challenge of axial distortion in LSFM by introducing an approach based on an auto-encoder trained on bead calibration images. The proposed pipeline demonstrates a complete solution, presenting promising results that surpass existing methods in denoising LSFM images. These advancements hold potential to significantly improve the interpretation of biological data.

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Image Reconstruction in Light-Sheet Microscopy: Spatially Varying Deconvolution and Mixed Noise

Cited by 8 publications

References 49 publications

3D exploration of gene expression in chicken embryos through combined RNA fluorescence in situ hybridization, immunofluorescence, and clearing

3D exploration of gene expression in chicken embryos through combined RNA fluorescence in situ hybridization, immunofluorescence, and clearing

Blind deconvolution in autocorrelation inversion for multiview light‐sheet microscopy

Distortion Correction and Denoising of Light Sheet Fluorescence Images

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