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membrane deconvolution target  (Softworx Inc)

 
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    Softworx Inc membrane deconvolution target
    a Example of <t>deconvolution</t> prediction with FM2FM. Top, input raw fluorescent membrane images (Raw FM 4-64); middle row, true deconvolved fluorescent membrane images (Deconvolved FM 4-64); bottom, FM2FM-predicted image. White squares mark regions zoomed in at right (Block 1 and Block 2). Diagonal dotted lines indicate profile traces in ( b ). Scale bars: full images, 10 µm; zoomed-in blocks, 5 µm. b Fluorescence profiles across the dotted lines in Block 1 (top) and Block 2 (bottom). Light blue, raw FM 4-64; orange, deconvolved FM 4-64; burgundy, FM2FM prediction. c , Structural similarity index measure (SSIM) between FM2FM predicted images and deconvolved images. The distribution of SSIM values across 74 image crops is shown. d Violin plots of cell width, length, surface area, volume, cross-sectional area, convex hull area, eccentricity and solidity calculated from deconvolved FM 4-64 images (orange) and FM2FM-predicted images (burgundy). The white dots and vertical lines within the violin plots represent the medians and standard deviations. P values from statistical comparisons between distributions (ANOVA or Kruskal test; see methods for details) are indicated in each panel. Twenty images (over 1000 cells) were processed and segmented with FMSeg. See the Methods for size calculation details.
    Membrane Deconvolution Target, supplied by Softworx Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/membrane+deconvolution+target/pmc13194920-323-6-9?v=Softworx+Inc
    Average 86 stars, based on 1 article reviews
    membrane deconvolution target - by Bioz Stars, 2026-07
    86/100 stars

    Images

    1) Product Images from "Deep-learning deconvolution and segmentation of fluorescent membranes for high-precision bacterial cell-size profiling"

    Article Title: Deep-learning deconvolution and segmentation of fluorescent membranes for high-precision bacterial cell-size profiling

    Journal: Communications Biology

    doi: 10.1038/s42003-026-10303-y

    a Example of deconvolution prediction with FM2FM. Top, input raw fluorescent membrane images (Raw FM 4-64); middle row, true deconvolved fluorescent membrane images (Deconvolved FM 4-64); bottom, FM2FM-predicted image. White squares mark regions zoomed in at right (Block 1 and Block 2). Diagonal dotted lines indicate profile traces in ( b ). Scale bars: full images, 10 µm; zoomed-in blocks, 5 µm. b Fluorescence profiles across the dotted lines in Block 1 (top) and Block 2 (bottom). Light blue, raw FM 4-64; orange, deconvolved FM 4-64; burgundy, FM2FM prediction. c , Structural similarity index measure (SSIM) between FM2FM predicted images and deconvolved images. The distribution of SSIM values across 74 image crops is shown. d Violin plots of cell width, length, surface area, volume, cross-sectional area, convex hull area, eccentricity and solidity calculated from deconvolved FM 4-64 images (orange) and FM2FM-predicted images (burgundy). The white dots and vertical lines within the violin plots represent the medians and standard deviations. P values from statistical comparisons between distributions (ANOVA or Kruskal test; see methods for details) are indicated in each panel. Twenty images (over 1000 cells) were processed and segmented with FMSeg. See the Methods for size calculation details.
    Figure Legend Snippet: a Example of deconvolution prediction with FM2FM. Top, input raw fluorescent membrane images (Raw FM 4-64); middle row, true deconvolved fluorescent membrane images (Deconvolved FM 4-64); bottom, FM2FM-predicted image. White squares mark regions zoomed in at right (Block 1 and Block 2). Diagonal dotted lines indicate profile traces in ( b ). Scale bars: full images, 10 µm; zoomed-in blocks, 5 µm. b Fluorescence profiles across the dotted lines in Block 1 (top) and Block 2 (bottom). Light blue, raw FM 4-64; orange, deconvolved FM 4-64; burgundy, FM2FM prediction. c , Structural similarity index measure (SSIM) between FM2FM predicted images and deconvolved images. The distribution of SSIM values across 74 image crops is shown. d Violin plots of cell width, length, surface area, volume, cross-sectional area, convex hull area, eccentricity and solidity calculated from deconvolved FM 4-64 images (orange) and FM2FM-predicted images (burgundy). The white dots and vertical lines within the violin plots represent the medians and standard deviations. P values from statistical comparisons between distributions (ANOVA or Kruskal test; see methods for details) are indicated in each panel. Twenty images (over 1000 cells) were processed and segmented with FMSeg. See the Methods for size calculation details.

    Techniques Used: Membrane, Blocking Assay, Fluorescence



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    membrane deconvolution target  (Softworx Inc)
    86
    Softworx Inc membrane deconvolution target
    a Example of <t>deconvolution</t> prediction with FM2FM. Top, input raw fluorescent membrane images (Raw FM 4-64); middle row, true deconvolved fluorescent membrane images (Deconvolved FM 4-64); bottom, FM2FM-predicted image. White squares mark regions zoomed in at right (Block 1 and Block 2). Diagonal dotted lines indicate profile traces in ( b ). Scale bars: full images, 10 µm; zoomed-in blocks, 5 µm. b Fluorescence profiles across the dotted lines in Block 1 (top) and Block 2 (bottom). Light blue, raw FM 4-64; orange, deconvolved FM 4-64; burgundy, FM2FM prediction. c , Structural similarity index measure (SSIM) between FM2FM predicted images and deconvolved images. The distribution of SSIM values across 74 image crops is shown. d Violin plots of cell width, length, surface area, volume, cross-sectional area, convex hull area, eccentricity and solidity calculated from deconvolved FM 4-64 images (orange) and FM2FM-predicted images (burgundy). The white dots and vertical lines within the violin plots represent the medians and standard deviations. P values from statistical comparisons between distributions (ANOVA or Kruskal test; see methods for details) are indicated in each panel. Twenty images (over 1000 cells) were processed and segmented with FMSeg. See the Methods for size calculation details.
    Membrane Deconvolution Target, supplied by Softworx Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/membrane+deconvolution+target/pmc13194920-323-6-9?v=Softworx+Inc
    Average 86 stars, based on 1 article reviews
    membrane deconvolution target - by Bioz Stars, 2026-07
    86/100 stars
    		  Buy from Supplier

    Image Search Results


    a Example of deconvolution prediction with FM2FM. Top, input raw fluorescent membrane images (Raw FM 4-64); middle row, true deconvolved fluorescent membrane images (Deconvolved FM 4-64); bottom, FM2FM-predicted image. White squares mark regions zoomed in at right (Block 1 and Block 2). Diagonal dotted lines indicate profile traces in ( b ). Scale bars: full images, 10 µm; zoomed-in blocks, 5 µm. b Fluorescence profiles across the dotted lines in Block 1 (top) and Block 2 (bottom). Light blue, raw FM 4-64; orange, deconvolved FM 4-64; burgundy, FM2FM prediction. c , Structural similarity index measure (SSIM) between FM2FM predicted images and deconvolved images. The distribution of SSIM values across 74 image crops is shown. d Violin plots of cell width, length, surface area, volume, cross-sectional area, convex hull area, eccentricity and solidity calculated from deconvolved FM 4-64 images (orange) and FM2FM-predicted images (burgundy). The white dots and vertical lines within the violin plots represent the medians and standard deviations. P values from statistical comparisons between distributions (ANOVA or Kruskal test; see methods for details) are indicated in each panel. Twenty images (over 1000 cells) were processed and segmented with FMSeg. See the Methods for size calculation details.

    Journal: Communications Biology

    Article Title: Deep-learning deconvolution and segmentation of fluorescent membranes for high-precision bacterial cell-size profiling

    doi: 10.1038/s42003-026-10303-y

    Figure Lengend Snippet: a Example of deconvolution prediction with FM2FM. Top, input raw fluorescent membrane images (Raw FM 4-64); middle row, true deconvolved fluorescent membrane images (Deconvolved FM 4-64); bottom, FM2FM-predicted image. White squares mark regions zoomed in at right (Block 1 and Block 2). Diagonal dotted lines indicate profile traces in ( b ). Scale bars: full images, 10 µm; zoomed-in blocks, 5 µm. b Fluorescence profiles across the dotted lines in Block 1 (top) and Block 2 (bottom). Light blue, raw FM 4-64; orange, deconvolved FM 4-64; burgundy, FM2FM prediction. c , Structural similarity index measure (SSIM) between FM2FM predicted images and deconvolved images. The distribution of SSIM values across 74 image crops is shown. d Violin plots of cell width, length, surface area, volume, cross-sectional area, convex hull area, eccentricity and solidity calculated from deconvolved FM 4-64 images (orange) and FM2FM-predicted images (burgundy). The white dots and vertical lines within the violin plots represent the medians and standard deviations. P values from statistical comparisons between distributions (ANOVA or Kruskal test; see methods for details) are indicated in each panel. Twenty images (over 1000 cells) were processed and segmented with FMSeg. See the Methods for size calculation details.

    Article Snippet: To compare model performance against the membrane deconvolution target (SoftWorx), we conducted qualitative assessments of output images' intensity profiles.

    Techniques: Membrane, Blocking Assay, Fluorescence