Review



patternquant semi-automated machine learning algorithm  (3DHistech ltd)

 
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    3DHistech ltd patternquant semi-automated machine learning algorithm
    Automated image analysis using the <t>PatternQuant</t> machine learning algorithm for measuring the proportions of fibrillin 1 immunoperoxidase reactions ( A,C, brown) within representative annotated areas of myelofibrotic bone marrow sections. Image segmentation reveals areas occupied by the immunoreactions (red), the immune‐negative tissue (green) and the cell‐free regions (yellow) ( B,D ). Higher power confirms the accuracy of segmentation ( B,D ). Highlighted numbers in ( D ) show measured areas in μm 2 and in % proportions. Graphs show the statistical correlations between fibrillin 1 quantitative results and Gomori's silver grades using both the Kruskal–Wallis test and Wilcoxon's post‐hoc test ( E ). The seven MF cases which were up‐scaled (red triangles) and six MF cases which were down‐scaled (green triangles) at visual scoring segregated to the upper and lower regions, respectively, within their MF categories in the box‐plots. The overlapping distribution curves of quantitative results within MF‐grades ( F ) confirms the continuous nature of myelofibrosis progression. [Color figure can be viewed at wileyonlinelibrary.com ]
    Patternquant Semi Automated Machine Learning Algorithm, supplied by 3DHistech ltd, 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/patternquant+semi-automated+machine+learning+algorithm/patternquant+semi+automated+machine+learning+algorithm/pmc10107930-35-11-22
    Average 90 stars, based on 1 article reviews
    patternquant semi-automated machine learning algorithm - by Bioz Stars, 2026-09
    90/100 stars

    Images

    1) Product Images from "Myelofibrosis progression grading based on type I and type III collagen and fibrillin 1 expression boosted by whole slide image analysis"

    Article Title: Myelofibrosis progression grading based on type I and type III collagen and fibrillin 1 expression boosted by whole slide image analysis

    Journal: Histopathology

    doi: 10.1111/his.14846

    Automated image analysis using the PatternQuant machine learning algorithm for measuring the proportions of fibrillin 1 immunoperoxidase reactions ( A,C, brown) within representative annotated areas of myelofibrotic bone marrow sections. Image segmentation reveals areas occupied by the immunoreactions (red), the immune‐negative tissue (green) and the cell‐free regions (yellow) ( B,D ). Higher power confirms the accuracy of segmentation ( B,D ). Highlighted numbers in ( D ) show measured areas in μm 2 and in % proportions. Graphs show the statistical correlations between fibrillin 1 quantitative results and Gomori's silver grades using both the Kruskal–Wallis test and Wilcoxon's post‐hoc test ( E ). The seven MF cases which were up‐scaled (red triangles) and six MF cases which were down‐scaled (green triangles) at visual scoring segregated to the upper and lower regions, respectively, within their MF categories in the box‐plots. The overlapping distribution curves of quantitative results within MF‐grades ( F ) confirms the continuous nature of myelofibrosis progression. [Color figure can be viewed at wileyonlinelibrary.com ]
    Figure Legend Snippet: Automated image analysis using the PatternQuant machine learning algorithm for measuring the proportions of fibrillin 1 immunoperoxidase reactions ( A,C, brown) within representative annotated areas of myelofibrotic bone marrow sections. Image segmentation reveals areas occupied by the immunoreactions (red), the immune‐negative tissue (green) and the cell‐free regions (yellow) ( B,D ). Higher power confirms the accuracy of segmentation ( B,D ). Highlighted numbers in ( D ) show measured areas in μm 2 and in % proportions. Graphs show the statistical correlations between fibrillin 1 quantitative results and Gomori's silver grades using both the Kruskal–Wallis test and Wilcoxon's post‐hoc test ( E ). The seven MF cases which were up‐scaled (red triangles) and six MF cases which were down‐scaled (green triangles) at visual scoring segregated to the upper and lower regions, respectively, within their MF categories in the box‐plots. The overlapping distribution curves of quantitative results within MF‐grades ( F ) confirms the continuous nature of myelofibrosis progression. [Color figure can be viewed at wileyonlinelibrary.com ]

    Techniques Used:

    Related Articles

    Software:

    Article Title: Myelofibrosis progression grading based on type I and type III collagen and fibrillin 1 expression boosted by whole slide image analysis
    Article Snippet: .. The image analysis of fibrillin 1 immunoreactions was performed using the PatternQuant semi‐automated machine learning algorithm of the QuantCenter software package (all 3DHistech). ..



    Similar Products

    patternquant semi-automated machine learning algorithm  (3DHistech ltd)
    90
    3DHistech ltd patternquant semi-automated machine learning algorithm
    Automated image analysis using the <t>PatternQuant</t> machine learning algorithm for measuring the proportions of fibrillin 1 immunoperoxidase reactions ( A,C, brown) within representative annotated areas of myelofibrotic bone marrow sections. Image segmentation reveals areas occupied by the immunoreactions (red), the immune‐negative tissue (green) and the cell‐free regions (yellow) ( B,D ). Higher power confirms the accuracy of segmentation ( B,D ). Highlighted numbers in ( D ) show measured areas in μm 2 and in % proportions. Graphs show the statistical correlations between fibrillin 1 quantitative results and Gomori's silver grades using both the Kruskal–Wallis test and Wilcoxon's post‐hoc test ( E ). The seven MF cases which were up‐scaled (red triangles) and six MF cases which were down‐scaled (green triangles) at visual scoring segregated to the upper and lower regions, respectively, within their MF categories in the box‐plots. The overlapping distribution curves of quantitative results within MF‐grades ( F ) confirms the continuous nature of myelofibrosis progression. [Color figure can be viewed at wileyonlinelibrary.com ]
    Patternquant Semi Automated Machine Learning Algorithm, supplied by 3DHistech ltd, 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/patternquant+semi-automated+machine+learning+algorithm/patternquant+semi+automated+machine+learning+algorithm/pmc10107930-35-11-22
    Average 90 stars, based on 1 article reviews
    patternquant semi-automated machine learning algorithm - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Automated image analysis using the PatternQuant machine learning algorithm for measuring the proportions of fibrillin 1 immunoperoxidase reactions ( A,C, brown) within representative annotated areas of myelofibrotic bone marrow sections. Image segmentation reveals areas occupied by the immunoreactions (red), the immune‐negative tissue (green) and the cell‐free regions (yellow) ( B,D ). Higher power confirms the accuracy of segmentation ( B,D ). Highlighted numbers in ( D ) show measured areas in μm 2 and in % proportions. Graphs show the statistical correlations between fibrillin 1 quantitative results and Gomori's silver grades using both the Kruskal–Wallis test and Wilcoxon's post‐hoc test ( E ). The seven MF cases which were up‐scaled (red triangles) and six MF cases which were down‐scaled (green triangles) at visual scoring segregated to the upper and lower regions, respectively, within their MF categories in the box‐plots. The overlapping distribution curves of quantitative results within MF‐grades ( F ) confirms the continuous nature of myelofibrosis progression. [Color figure can be viewed at wileyonlinelibrary.com ]

    Journal: Histopathology

    Article Title: Myelofibrosis progression grading based on type I and type III collagen and fibrillin 1 expression boosted by whole slide image analysis

    doi: 10.1111/his.14846

    Figure Lengend Snippet: Automated image analysis using the PatternQuant machine learning algorithm for measuring the proportions of fibrillin 1 immunoperoxidase reactions ( A,C, brown) within representative annotated areas of myelofibrotic bone marrow sections. Image segmentation reveals areas occupied by the immunoreactions (red), the immune‐negative tissue (green) and the cell‐free regions (yellow) ( B,D ). Higher power confirms the accuracy of segmentation ( B,D ). Highlighted numbers in ( D ) show measured areas in μm 2 and in % proportions. Graphs show the statistical correlations between fibrillin 1 quantitative results and Gomori's silver grades using both the Kruskal–Wallis test and Wilcoxon's post‐hoc test ( E ). The seven MF cases which were up‐scaled (red triangles) and six MF cases which were down‐scaled (green triangles) at visual scoring segregated to the upper and lower regions, respectively, within their MF categories in the box‐plots. The overlapping distribution curves of quantitative results within MF‐grades ( F ) confirms the continuous nature of myelofibrosis progression. [Color figure can be viewed at wileyonlinelibrary.com ]

    Article Snippet: The image analysis of fibrillin 1 immunoreactions was performed using the PatternQuant semi‐automated machine learning algorithm of the QuantCenter software package (all 3DHistech).

    Techniques: