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in-house matlab program 2021a  (MathWorks Inc)


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    MathWorks Inc in-house matlab program 2021a
    CartoCell pipeline for high-content epithelial cysts segmentation Phase 1: the “high-resolution raw images” consist of confocal z-stack images, where the cell membrane is stained. These images are segmented and proofread using LimeSeg and a custom <t>MATLAB</t> code for curation to obtain the “high-resolution label images” . Together, the raw and the label images encompass the “training high-resolution dataset.” Number of samples = 21. Phase 2: the “training high-resolution dataset” is down-sampled to obtain the “training down-sampled dataset,” which is the training set for the “model M1.” Phase 3: low-resolution images are obtained from confocal z-stack images, stained in a similar way to phase 1. Number of samples = 293. Scale bar, 100 μm. Next, the “work-flow M1” is applied: inference using “model M1” and subsequent post-processing to obtain individual cell instance predictions and cell masks, followed by the 3D Voronoi algorithm to guarantee that predicted cells remain in close contact. As a result, the “low-resolution label images” are generated. Phase 4: training of the “model M2” on the large “training low-resolution dataset.” Number of samples = 314. Phase 5: high-content segmentation of new low-resolution images (unseen by the pipeline) using the “work-flow M2,” which is equivalent to the “work-flow M1” but using the “model M2.” See also <xref ref-type=Figures S1–S3 ; Tables S2 , , and . " width="250" height="auto" />
    In House Matlab Program 2021a, supplied by MathWorks Inc, 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/result/in-house matlab program 2021a/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    in-house matlab program 2021a - by Bioz Stars, 2026-04
    90/100 stars

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    1) Product Images from "CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia"

    Article Title: CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia

    Journal: Cell Reports Methods

    doi: 10.1016/j.crmeth.2023.100597

    CartoCell pipeline for high-content epithelial cysts segmentation Phase 1: the “high-resolution raw images” consist of confocal z-stack images, where the cell membrane is stained. These images are segmented and proofread using LimeSeg and a custom MATLAB code for curation to obtain the “high-resolution label images” . Together, the raw and the label images encompass the “training high-resolution dataset.” Number of samples = 21. Phase 2: the “training high-resolution dataset” is down-sampled to obtain the “training down-sampled dataset,” which is the training set for the “model M1.” Phase 3: low-resolution images are obtained from confocal z-stack images, stained in a similar way to phase 1. Number of samples = 293. Scale bar, 100 μm. Next, the “work-flow M1” is applied: inference using “model M1” and subsequent post-processing to obtain individual cell instance predictions and cell masks, followed by the 3D Voronoi algorithm to guarantee that predicted cells remain in close contact. As a result, the “low-resolution label images” are generated. Phase 4: training of the “model M2” on the large “training low-resolution dataset.” Number of samples = 314. Phase 5: high-content segmentation of new low-resolution images (unseen by the pipeline) using the “work-flow M2,” which is equivalent to the “work-flow M1” but using the “model M2.” See also <xref ref-type=Figures S1–S3 ; Tables S2 , , and . " title="... segmented and proofread using LimeSeg and a custom MATLAB code for curation to obtain the “high-resolution label ..." property="contentUrl" width="100%" height="100%"/>
    Figure Legend Snippet: CartoCell pipeline for high-content epithelial cysts segmentation Phase 1: the “high-resolution raw images” consist of confocal z-stack images, where the cell membrane is stained. These images are segmented and proofread using LimeSeg and a custom MATLAB code for curation to obtain the “high-resolution label images” . Together, the raw and the label images encompass the “training high-resolution dataset.” Number of samples = 21. Phase 2: the “training high-resolution dataset” is down-sampled to obtain the “training down-sampled dataset,” which is the training set for the “model M1.” Phase 3: low-resolution images are obtained from confocal z-stack images, stained in a similar way to phase 1. Number of samples = 293. Scale bar, 100 μm. Next, the “work-flow M1” is applied: inference using “model M1” and subsequent post-processing to obtain individual cell instance predictions and cell masks, followed by the 3D Voronoi algorithm to guarantee that predicted cells remain in close contact. As a result, the “low-resolution label images” are generated. Phase 4: training of the “model M2” on the large “training low-resolution dataset.” Number of samples = 314. Phase 5: high-content segmentation of new low-resolution images (unseen by the pipeline) using the “work-flow M2,” which is equivalent to the “work-flow M1” but using the “model M2.” See also Figures S1–S3 ; Tables S2 , , and .

    Techniques Used: Membrane, Staining, Generated


    Figure Legend Snippet:

    Techniques Used: Recombinant, Software



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    in-house matlab program 2021a  (MathWorks Inc)
    90
    MathWorks Inc in-house matlab program 2021a
    CartoCell pipeline for high-content epithelial cysts segmentation Phase 1: the “high-resolution raw images” consist of confocal z-stack images, where the cell membrane is stained. These images are segmented and proofread using LimeSeg and a custom <t>MATLAB</t> code for curation to obtain the “high-resolution label images” . Together, the raw and the label images encompass the “training high-resolution dataset.” Number of samples = 21. Phase 2: the “training high-resolution dataset” is down-sampled to obtain the “training down-sampled dataset,” which is the training set for the “model M1.” Phase 3: low-resolution images are obtained from confocal z-stack images, stained in a similar way to phase 1. Number of samples = 293. Scale bar, 100 μm. Next, the “work-flow M1” is applied: inference using “model M1” and subsequent post-processing to obtain individual cell instance predictions and cell masks, followed by the 3D Voronoi algorithm to guarantee that predicted cells remain in close contact. As a result, the “low-resolution label images” are generated. Phase 4: training of the “model M2” on the large “training low-resolution dataset.” Number of samples = 314. Phase 5: high-content segmentation of new low-resolution images (unseen by the pipeline) using the “work-flow M2,” which is equivalent to the “work-flow M1” but using the “model M2.” See also <xref ref-type=Figures S1–S3 ; Tables S2 , , and . " width="250" height="auto" />
    In House Matlab Program 2021a, supplied by MathWorks Inc, 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/result/in-house matlab program 2021a/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    in-house matlab program 2021a - by Bioz Stars, 2026-04
    90/100 stars
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    Image Search Results


    CartoCell pipeline for high-content epithelial cysts segmentation Phase 1: the “high-resolution raw images” consist of confocal z-stack images, where the cell membrane is stained. These images are segmented and proofread using LimeSeg and a custom MATLAB code for curation to obtain the “high-resolution label images” . Together, the raw and the label images encompass the “training high-resolution dataset.” Number of samples = 21. Phase 2: the “training high-resolution dataset” is down-sampled to obtain the “training down-sampled dataset,” which is the training set for the “model M1.” Phase 3: low-resolution images are obtained from confocal z-stack images, stained in a similar way to phase 1. Number of samples = 293. Scale bar, 100 μm. Next, the “work-flow M1” is applied: inference using “model M1” and subsequent post-processing to obtain individual cell instance predictions and cell masks, followed by the 3D Voronoi algorithm to guarantee that predicted cells remain in close contact. As a result, the “low-resolution label images” are generated. Phase 4: training of the “model M2” on the large “training low-resolution dataset.” Number of samples = 314. Phase 5: high-content segmentation of new low-resolution images (unseen by the pipeline) using the “work-flow M2,” which is equivalent to the “work-flow M1” but using the “model M2.” See also <xref ref-type=Figures S1–S3 ; Tables S2 , , and . " width="100%" height="100%">

    Journal: Cell Reports Methods

    Article Title: CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia

    doi: 10.1016/j.crmeth.2023.100597

    Figure Lengend Snippet: CartoCell pipeline for high-content epithelial cysts segmentation Phase 1: the “high-resolution raw images” consist of confocal z-stack images, where the cell membrane is stained. These images are segmented and proofread using LimeSeg and a custom MATLAB code for curation to obtain the “high-resolution label images” . Together, the raw and the label images encompass the “training high-resolution dataset.” Number of samples = 21. Phase 2: the “training high-resolution dataset” is down-sampled to obtain the “training down-sampled dataset,” which is the training set for the “model M1.” Phase 3: low-resolution images are obtained from confocal z-stack images, stained in a similar way to phase 1. Number of samples = 293. Scale bar, 100 μm. Next, the “work-flow M1” is applied: inference using “model M1” and subsequent post-processing to obtain individual cell instance predictions and cell masks, followed by the 3D Voronoi algorithm to guarantee that predicted cells remain in close contact. As a result, the “low-resolution label images” are generated. Phase 4: training of the “model M2” on the large “training low-resolution dataset.” Number of samples = 314. Phase 5: high-content segmentation of new low-resolution images (unseen by the pipeline) using the “work-flow M2,” which is equivalent to the “work-flow M1” but using the “model M2.” See also Figures S1–S3 ; Tables S2 , , and .

    Article Snippet: The output of LimeSeg was processed using an in-house MATLAB program (2021a MathWorks) to detect and curate imperfections during cysts segmentation (see section).

    Techniques: Membrane, Staining, Generated

    Journal: Cell Reports Methods

    Article Title: CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia

    doi: 10.1016/j.crmeth.2023.100597

    Figure Lengend Snippet:

    Article Snippet: The output of LimeSeg was processed using an in-house MATLAB program (2021a MathWorks) to detect and curate imperfections during cysts segmentation (see section).

    Techniques: Recombinant, Software