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subcellular segmentation  (KNIME GmbH)

 
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    Structured Review

    KNIME GmbH subcellular segmentation
    NR-SAT single-cell segmentation and tracking scheme. (Phase 1) The input nuclear channel is illumination-corrected using background subtraction, followed by local contrast enhancement. The resulting images then undergo thresholding (Mean method) and are dilated and then labeled. An object filter is applied to remove threshold artifacts prior to converting the images into binary images and filling holes. Finally, the data is passed to the Wählby Cell Clump Splitter, which separates nuclei that are in proximity to one another. (Phase 2) These data are then passed to the nuclei tracking nodes where the <t>ImageJ</t> plugin TrackMate is implemented to track each cell, accounting for splitting and merging events. (Phase 3) After the nuclei are tracked, the nuclear mask is duplicated and dilated (number of dilations is user-defined and applied to all images in the same manner, 10× in this example) to generate two masks, where the newly dilated mask is larger than the original source mask. These two masks (the newly larger dilated mask and the smaller original mask) are then segmented via the Voronoi segmentation node, generating cytoplasmic rings that are approximately a fixed pixel-width. (Phase 4) Finally these cytoplasmic rings, along with the nuclear masks, are applied to the measurement channel(s) and written to a CSV output file.
    Subcellular Segmentation, supplied by KNIME GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/subcellular+segmentation/pmc09145009-73-2-1?v=KNIME+GmbH
    Average 90 stars, based on 1 article reviews
    subcellular segmentation - by Bioz Stars, 2026-07
    90/100 stars

    Images

    1) Product Images from "HIV RGB: Automated Single-Cell Analysis of HIV-1 Rev-Dependent RNA Nuclear Export and Translation Using Image Processing in KNIME"

    Article Title: HIV RGB: Automated Single-Cell Analysis of HIV-1 Rev-Dependent RNA Nuclear Export and Translation Using Image Processing in KNIME

    Journal: Viruses

    doi: 10.3390/v14050903

    NR-SAT single-cell segmentation and tracking scheme. (Phase 1) The input nuclear channel is illumination-corrected using background subtraction, followed by local contrast enhancement. The resulting images then undergo thresholding (Mean method) and are dilated and then labeled. An object filter is applied to remove threshold artifacts prior to converting the images into binary images and filling holes. Finally, the data is passed to the Wählby Cell Clump Splitter, which separates nuclei that are in proximity to one another. (Phase 2) These data are then passed to the nuclei tracking nodes where the ImageJ plugin TrackMate is implemented to track each cell, accounting for splitting and merging events. (Phase 3) After the nuclei are tracked, the nuclear mask is duplicated and dilated (number of dilations is user-defined and applied to all images in the same manner, 10× in this example) to generate two masks, where the newly dilated mask is larger than the original source mask. These two masks (the newly larger dilated mask and the smaller original mask) are then segmented via the Voronoi segmentation node, generating cytoplasmic rings that are approximately a fixed pixel-width. (Phase 4) Finally these cytoplasmic rings, along with the nuclear masks, are applied to the measurement channel(s) and written to a CSV output file.
    Figure Legend Snippet: NR-SAT single-cell segmentation and tracking scheme. (Phase 1) The input nuclear channel is illumination-corrected using background subtraction, followed by local contrast enhancement. The resulting images then undergo thresholding (Mean method) and are dilated and then labeled. An object filter is applied to remove threshold artifacts prior to converting the images into binary images and filling holes. Finally, the data is passed to the Wählby Cell Clump Splitter, which separates nuclei that are in proximity to one another. (Phase 2) These data are then passed to the nuclei tracking nodes where the ImageJ plugin TrackMate is implemented to track each cell, accounting for splitting and merging events. (Phase 3) After the nuclei are tracked, the nuclear mask is duplicated and dilated (number of dilations is user-defined and applied to all images in the same manner, 10× in this example) to generate two masks, where the newly dilated mask is larger than the original source mask. These two masks (the newly larger dilated mask and the smaller original mask) are then segmented via the Voronoi segmentation node, generating cytoplasmic rings that are approximately a fixed pixel-width. (Phase 4) Finally these cytoplasmic rings, along with the nuclear masks, are applied to the measurement channel(s) and written to a CSV output file.

    Techniques Used: Labeling



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    subcellular segmentation  (KNIME GmbH)
    90
    KNIME GmbH subcellular segmentation
    NR-SAT single-cell segmentation and tracking scheme. (Phase 1) The input nuclear channel is illumination-corrected using background subtraction, followed by local contrast enhancement. The resulting images then undergo thresholding (Mean method) and are dilated and then labeled. An object filter is applied to remove threshold artifacts prior to converting the images into binary images and filling holes. Finally, the data is passed to the Wählby Cell Clump Splitter, which separates nuclei that are in proximity to one another. (Phase 2) These data are then passed to the nuclei tracking nodes where the <t>ImageJ</t> plugin TrackMate is implemented to track each cell, accounting for splitting and merging events. (Phase 3) After the nuclei are tracked, the nuclear mask is duplicated and dilated (number of dilations is user-defined and applied to all images in the same manner, 10× in this example) to generate two masks, where the newly dilated mask is larger than the original source mask. These two masks (the newly larger dilated mask and the smaller original mask) are then segmented via the Voronoi segmentation node, generating cytoplasmic rings that are approximately a fixed pixel-width. (Phase 4) Finally these cytoplasmic rings, along with the nuclear masks, are applied to the measurement channel(s) and written to a CSV output file.
    Subcellular Segmentation, supplied by KNIME GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/subcellular+segmentation/pmc09145009-73-2-1?v=KNIME+GmbH
    Average 90 stars, based on 1 article reviews
    subcellular segmentation - by Bioz Stars, 2026-07
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    NR-SAT single-cell segmentation and tracking scheme. (Phase 1) The input nuclear channel is illumination-corrected using background subtraction, followed by local contrast enhancement. The resulting images then undergo thresholding (Mean method) and are dilated and then labeled. An object filter is applied to remove threshold artifacts prior to converting the images into binary images and filling holes. Finally, the data is passed to the Wählby Cell Clump Splitter, which separates nuclei that are in proximity to one another. (Phase 2) These data are then passed to the nuclei tracking nodes where the ImageJ plugin TrackMate is implemented to track each cell, accounting for splitting and merging events. (Phase 3) After the nuclei are tracked, the nuclear mask is duplicated and dilated (number of dilations is user-defined and applied to all images in the same manner, 10× in this example) to generate two masks, where the newly dilated mask is larger than the original source mask. These two masks (the newly larger dilated mask and the smaller original mask) are then segmented via the Voronoi segmentation node, generating cytoplasmic rings that are approximately a fixed pixel-width. (Phase 4) Finally these cytoplasmic rings, along with the nuclear masks, are applied to the measurement channel(s) and written to a CSV output file.

    Journal: Viruses

    Article Title: HIV RGB: Automated Single-Cell Analysis of HIV-1 Rev-Dependent RNA Nuclear Export and Translation Using Image Processing in KNIME

    doi: 10.3390/v14050903

    Figure Lengend Snippet: NR-SAT single-cell segmentation and tracking scheme. (Phase 1) The input nuclear channel is illumination-corrected using background subtraction, followed by local contrast enhancement. The resulting images then undergo thresholding (Mean method) and are dilated and then labeled. An object filter is applied to remove threshold artifacts prior to converting the images into binary images and filling holes. Finally, the data is passed to the Wählby Cell Clump Splitter, which separates nuclei that are in proximity to one another. (Phase 2) These data are then passed to the nuclei tracking nodes where the ImageJ plugin TrackMate is implemented to track each cell, accounting for splitting and merging events. (Phase 3) After the nuclei are tracked, the nuclear mask is duplicated and dilated (number of dilations is user-defined and applied to all images in the same manner, 10× in this example) to generate two masks, where the newly dilated mask is larger than the original source mask. These two masks (the newly larger dilated mask and the smaller original mask) are then segmented via the Voronoi segmentation node, generating cytoplasmic rings that are approximately a fixed pixel-width. (Phase 4) Finally these cytoplasmic rings, along with the nuclear masks, are applied to the measurement channel(s) and written to a CSV output file.

    Article Snippet: The KNIME Image Processing extension integrates ImageJ image pre-processing, subcellular segmentation, cell tracking, and extraction of cellular morphological feature descriptors for single-cell analysis [ ].

    Techniques: Labeling