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calcium image processing toolbox caiman matlab  (MathWorks Inc)


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    Structured Review

    MathWorks Inc calcium image processing toolbox caiman matlab
    Calcium Image Processing Toolbox Caiman Matlab, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2914 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/non-negative+matrix+factorization+function+nnmf/Image+Processing+Toolbox/pmc09046249-254-4-8
    Average 96 stars, based on 2914 article reviews
    calcium image processing toolbox caiman matlab - by Bioz Stars, 2026-10
    96/100 stars

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    Article Title: Hbs and Rst adhesion molecules provide a regional code that regulates cell elimination during epithelial remodeling
    Article Snippet: MATLAB with Image processing toolbox and Statistics and Machine learning toolbox , Mathworks , https://www.mathworks.com/.

    Article Title: Blocking microglial reactivity via purinergic receptors prevents subacute cognitive deficits after TIA.
    Article Snippet: For the microglia morphology analysis, confocal Z-stack images were processed, and microglial morphology features were extracted using custom-written scripts in MATLAB (R216b, The MathWorks, Natick, Massachusetts, USA), with dependencies on the Image Processing Toolbox as well as Statistics and Machine Learning Toolbox.

    Article Title: Fast-scanning small-angle X-ray scattering of hydrated biological cells
    Article Snippet: All data analysis is performed using MATLAB R2020a (The MathWorks, Inc., Natick, MA, USA) scripts, including the Image Processing Toolbox and functions of the Nanodiffraction Toolbox published by Nicolas et al. (2017).

    Article Title: Association of Maternal Antenatal Distress with Child Amygdala-Prefrontal Cortex Functional Connectivity at 2 - 3 Years in a South African Birth Cohort Study.
    Article Snippet: PFC parcellated areas were grouped according to their anatomical locations into four larger areas as the regions of interest (ROIs) (Figure 2) using the MATLAB (R2023b) image processing toolbox (version 23.2) [48].

    Article Title: A quantitative framework for multiscale analysis of Candida albicans biofilm development
    Article Snippet: Image processing in MATLAB We use MATLAB’s image processing toolbox to analyse experimental images for the hyphal tracking, area coverage, and biofilm front light-sheet analyses presented in this work.

    Software:

    Article Title: System and method for improving the clarity of overlapping objects
    Article Snippet: .. Such image processing software includes, but is not limited to, YOLO (real-time object detection algorithm that can identify objects and draw bounding boxes in a single pass through the image), Faster R-CNN (A deep learning-based framework that detects objects by proposing regions of interest and refining their boundaries), Single Shot MultiBox Detector (Detects objects and their bounding boxes using a single forward pass of a convolutional neural network), OpenCV (An open-source computer vision library that includes functions for image processing and drawing bounding boxes, though typically combined with machine learning models for detection), and MATLAB Image Processing Toolbox (tools for detecting and marking objects or text regions with bounding boxes, suitable for academic and research purposes). ..

    Refining:

    Article Title: System and method for improving the clarity of overlapping objects
    Article Snippet: .. Such image processing software includes, but is not limited to, YOLO (real-time object detection algorithm that can identify objects and draw bounding boxes in a single pass through the image), Faster R-CNN (A deep learning-based framework that detects objects by proposing regions of interest and refining their boundaries), Single Shot MultiBox Detector (Detects objects and their bounding boxes using a single forward pass of a convolutional neural network), OpenCV (An open-source computer vision library that includes functions for image processing and drawing bounding boxes, though typically combined with machine learning models for detection), and MATLAB Image Processing Toolbox (tools for detecting and marking objects or text regions with bounding boxes, suitable for academic and research purposes). ..



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    Histology and immunohistochemical analysis of explanted scaffolds. (a) Representative micrographs of histological sections showing Hematoxylin/Eosin (H&E), human vimentin (hVim) and Pico Sirius Red (PSR) stains after 16 weeks subcutaneous implantation in mice. H&E and hVim images are shown at approximately identical locations on the samples. PSR images represent entire scaffolds by stitched together micrographs. (b) , Quantitative analysis of collagen content from Raman spectroscopy <t>(NNMF</t> Component 3) and PSR images after 16 weeks of implantation. Collagen (Coll.) content from PSR histology is represented by a ratio of total PSR positive area divided by total scaffold cavity perimeter (Area-to-perimeter ratio). Histology derived measures of collagen content correlate well with both ex vivo and in vivo Raman derived collagen estimates. Pearson's correlation coefficient (r). Scales bars: 200 µm (H&E, hVim micrographs) and 1 mm (PSR micrographs). Groups: scaffolds without (No cells ) human mesenchymal stem cells (hMSCs), with hMSCs (MSC 1.5 , MSC 7.5 , MSC 7.5 +GF ) amount indicated by subscript ( e.g. 7.5 = 7.5 × 10 5 cells per scaffold). hMSCs preconditioned with BMP2 growth factor for 24 h prior to implantation (+GF).
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    Histology and immunohistochemical analysis of explanted scaffolds. (a) Representative micrographs of histological sections showing Hematoxylin/Eosin (H&E), human vimentin (hVim) and Pico Sirius Red (PSR) stains after 16 weeks subcutaneous implantation in mice. H&E and hVim images are shown at approximately identical locations on the samples. PSR images represent entire scaffolds by stitched together micrographs. (b) , Quantitative analysis of collagen content from Raman spectroscopy <t>(NNMF</t> Component 3) and PSR images after 16 weeks of implantation. Collagen (Coll.) content from PSR histology is represented by a ratio of total PSR positive area divided by total scaffold cavity perimeter (Area-to-perimeter ratio). Histology derived measures of collagen content correlate well with both ex vivo and in vivo Raman derived collagen estimates. Pearson's correlation coefficient (r). Scales bars: 200 µm (H&E, hVim micrographs) and 1 mm (PSR micrographs). Groups: scaffolds without (No cells ) human mesenchymal stem cells (hMSCs), with hMSCs (MSC 1.5 , MSC 7.5 , MSC 7.5 +GF ) amount indicated by subscript ( e.g. 7.5 = 7.5 × 10 5 cells per scaffold). hMSCs preconditioned with BMP2 growth factor for 24 h prior to implantation (+GF).
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    Image Search Results


    Histology and immunohistochemical analysis of explanted scaffolds. (a) Representative micrographs of histological sections showing Hematoxylin/Eosin (H&E), human vimentin (hVim) and Pico Sirius Red (PSR) stains after 16 weeks subcutaneous implantation in mice. H&E and hVim images are shown at approximately identical locations on the samples. PSR images represent entire scaffolds by stitched together micrographs. (b) , Quantitative analysis of collagen content from Raman spectroscopy (NNMF Component 3) and PSR images after 16 weeks of implantation. Collagen (Coll.) content from PSR histology is represented by a ratio of total PSR positive area divided by total scaffold cavity perimeter (Area-to-perimeter ratio). Histology derived measures of collagen content correlate well with both ex vivo and in vivo Raman derived collagen estimates. Pearson's correlation coefficient (r). Scales bars: 200 µm (H&E, hVim micrographs) and 1 mm (PSR micrographs). Groups: scaffolds without (No cells ) human mesenchymal stem cells (hMSCs), with hMSCs (MSC 1.5 , MSC 7.5 , MSC 7.5 +GF ) amount indicated by subscript ( e.g. 7.5 = 7.5 × 10 5 cells per scaffold). hMSCs preconditioned with BMP2 growth factor for 24 h prior to implantation (+GF).

    Journal: Biomaterials and Biosystems

    Article Title: In vivo non-invasive monitoring of tissue development in 3D printed subcutaneous bone scaffolds using fibre-optic Raman spectroscopy

    doi: 10.1016/j.bbiosy.2022.100059

    Figure Lengend Snippet: Histology and immunohistochemical analysis of explanted scaffolds. (a) Representative micrographs of histological sections showing Hematoxylin/Eosin (H&E), human vimentin (hVim) and Pico Sirius Red (PSR) stains after 16 weeks subcutaneous implantation in mice. H&E and hVim images are shown at approximately identical locations on the samples. PSR images represent entire scaffolds by stitched together micrographs. (b) , Quantitative analysis of collagen content from Raman spectroscopy (NNMF Component 3) and PSR images after 16 weeks of implantation. Collagen (Coll.) content from PSR histology is represented by a ratio of total PSR positive area divided by total scaffold cavity perimeter (Area-to-perimeter ratio). Histology derived measures of collagen content correlate well with both ex vivo and in vivo Raman derived collagen estimates. Pearson's correlation coefficient (r). Scales bars: 200 µm (H&E, hVim micrographs) and 1 mm (PSR micrographs). Groups: scaffolds without (No cells ) human mesenchymal stem cells (hMSCs), with hMSCs (MSC 1.5 , MSC 7.5 , MSC 7.5 +GF ) amount indicated by subscript ( e.g. 7.5 = 7.5 × 10 5 cells per scaffold). hMSCs preconditioned with BMP2 growth factor for 24 h prior to implantation (+GF).

    Article Snippet: Following pre-processing, spectral models were developed using the MATLAB statistics toolbox function non-negative matrix factorization (NNMF) ( c) with in vivo spectra ( a), ex vivo , and reference spectra ( b) as input.

    Techniques: Immunohistochemical staining, Raman Spectroscopy, Derivative Assay, Ex Vivo, In Vivo