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nis elements deconvolution software  (Nikon)


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    Nikon nis elements deconvolution software
    Nis Elements Deconvolution Software, supplied by Nikon, used in various techniques. Bioz Stars score: 99/100, based on 37545 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/nis elements deconvolution software/product/Nikon
    Average 99 stars, based on 37545 article reviews
    nis elements deconvolution software - by Bioz Stars, 2026-04
    99/100 stars

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    Fig. 1 | Implementation and benchmarking of Deconwolf. a, Schematic Deconwolf workflow. WSI, whole-slide image. b,c, In silico generated microtubule images before (ground truth) (b) and after adding artificial noise to simulate a real image (c). Maximum z-projection is shown. d, MSE after deconvolving the image in c using the default Deconwolf mode with scaled heavy ball15 acceleration (DW_SHB), or Deconwolf based on the classic Richardson–Lucy <t>deconvolution</t> method (DW_RL)2,3. The dashed vertical lines indicate the number of iterations needed to reach the minimum MSE. e, As in c after deconvolution with Deconwolf (DW) using default settings. it, number of iterations. t, deconvolution time measured on an 8-Core AMD Ryzen 7 3700X machine. f, As in c using DeconvolutionLab2 (DL2) with default settings at 115 iterations. g, As in
    Deconvolution Software, supplied by Nikon, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Fig. 1 | Implementation and benchmarking of Deconwolf. a, Schematic Deconwolf workflow. WSI, whole-slide image. b,c, In silico generated microtubule images before (ground truth) (b) and after adding artificial noise to simulate a real image (c). Maximum z-projection is shown. d, MSE after deconvolving the image in c using the default Deconwolf mode with scaled heavy ball15 acceleration (DW_SHB), or Deconwolf based on the classic Richardson–Lucy <t>deconvolution</t> method (DW_RL)2,3. The dashed vertical lines indicate the number of iterations needed to reach the minimum MSE. e, As in c after deconvolution with Deconwolf (DW) using default settings. it, number of iterations. t, deconvolution time measured on an 8-Core AMD Ryzen 7 3700X machine. f, As in c using DeconvolutionLab2 (DL2) with default settings at 115 iterations. g, As in
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    Nikon nis element deconvolution software
    Fig. 1 | Implementation and benchmarking of Deconwolf. a, Schematic Deconwolf workflow. WSI, whole-slide image. b,c, In silico generated microtubule images before (ground truth) (b) and after adding artificial noise to simulate a real image (c). Maximum z-projection is shown. d, MSE after deconvolving the image in c using the default Deconwolf mode with scaled heavy ball15 acceleration (DW_SHB), or Deconwolf based on the classic Richardson–Lucy <t>deconvolution</t> method (DW_RL)2,3. The dashed vertical lines indicate the number of iterations needed to reach the minimum MSE. e, As in c after deconvolution with Deconwolf (DW) using default settings. it, number of iterations. t, deconvolution time measured on an 8-Core AMD Ryzen 7 3700X machine. f, As in c using DeconvolutionLab2 (DL2) with default settings at 115 iterations. g, As in
    Nis Element Deconvolution Software, supplied by Nikon, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/nis element deconvolution software/product/Nikon
    Average 99 stars, based on 1 article reviews
    nis element deconvolution software - by Bioz Stars, 2026-04
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    Fig. 1 | Implementation and benchmarking of Deconwolf. a, Schematic Deconwolf workflow. WSI, whole-slide image. b,c, In silico generated microtubule images before (ground truth) (b) and after adding artificial noise to simulate a real image (c). Maximum z-projection is shown. d, MSE after deconvolving the image in c using the default Deconwolf mode with scaled heavy ball15 acceleration (DW_SHB), or Deconwolf based on the classic Richardson–Lucy deconvolution method (DW_RL)2,3. The dashed vertical lines indicate the number of iterations needed to reach the minimum MSE. e, As in c after deconvolution with Deconwolf (DW) using default settings. it, number of iterations. t, deconvolution time measured on an 8-Core AMD Ryzen 7 3700X machine. f, As in c using DeconvolutionLab2 (DL2) with default settings at 115 iterations. g, As in

    Journal: Nature methods

    Article Title: Deconwolf enables high-performance deconvolution of widefield fluorescence microscopy images.

    doi: 10.1038/s41592-024-02294-7

    Figure Lengend Snippet: Fig. 1 | Implementation and benchmarking of Deconwolf. a, Schematic Deconwolf workflow. WSI, whole-slide image. b,c, In silico generated microtubule images before (ground truth) (b) and after adding artificial noise to simulate a real image (c). Maximum z-projection is shown. d, MSE after deconvolving the image in c using the default Deconwolf mode with scaled heavy ball15 acceleration (DW_SHB), or Deconwolf based on the classic Richardson–Lucy deconvolution method (DW_RL)2,3. The dashed vertical lines indicate the number of iterations needed to reach the minimum MSE. e, As in c after deconvolution with Deconwolf (DW) using default settings. it, number of iterations. t, deconvolution time measured on an 8-Core AMD Ryzen 7 3700X machine. f, As in c using DeconvolutionLab2 (DL2) with default settings at 115 iterations. g, As in

    Article Snippet: Visual inspection of the images in the original dataset showed densely packed clouds of fluorescent dots in different colors inside each nucleus, which could be only partially resolved by applying the commercial deconvolution software (Nikon NIS Elements AR, v5.02.0) incorporated in the OligoFISSEQ image processing pipeline (Fig. 6c).

    Techniques: In Silico, Generated

    Fig. 5 | Deconwolf enables robust detection of individual transcripts in low-magnification smFISH images. a, Breast adenocarcinoma tissue section stained with an smFISH probe targeting MKI67 transcripts (white) and imaged on a widefield microscope using a ×20 air objective, after deconvolution with Deconwolf (DW). Maximum z-projection is shown. Blue, DNA. Scale bars, 200 μm in the large left panel; 10 μm in the two small panels on the right. b,c, Zoom-in of the regions (R1 and R2) marked by the white dashed squares in a. Scale bars, 10 μm. d, Left plot: probability density function (PDF) of smFISH dot fluorescence

    Journal: Nature methods

    Article Title: Deconwolf enables high-performance deconvolution of widefield fluorescence microscopy images.

    doi: 10.1038/s41592-024-02294-7

    Figure Lengend Snippet: Fig. 5 | Deconwolf enables robust detection of individual transcripts in low-magnification smFISH images. a, Breast adenocarcinoma tissue section stained with an smFISH probe targeting MKI67 transcripts (white) and imaged on a widefield microscope using a ×20 air objective, after deconvolution with Deconwolf (DW). Maximum z-projection is shown. Blue, DNA. Scale bars, 200 μm in the large left panel; 10 μm in the two small panels on the right. b,c, Zoom-in of the regions (R1 and R2) marked by the white dashed squares in a. Scale bars, 10 μm. d, Left plot: probability density function (PDF) of smFISH dot fluorescence

    Article Snippet: Visual inspection of the images in the original dataset showed densely packed clouds of fluorescent dots in different colors inside each nucleus, which could be only partially resolved by applying the commercial deconvolution software (Nikon NIS Elements AR, v5.02.0) incorporated in the OligoFISSEQ image processing pipeline (Fig. 6c).

    Techniques: Staining, Microscopy, Fluorescence