scholarly journals Associative image analysis: A method for automated quantification of 3D multi-parameter images of brain tissue

2008 ◽  
Vol 170 (1) ◽  
pp. 165-178 ◽  
Author(s):  
Christopher S. Bjornsson ◽  
Gang Lin ◽  
Yousef Al-Kofahi ◽  
Arunachalam Narayanaswamy ◽  
Karen L. Smith ◽  
...  
Keyword(s):  
Image Analysis ◽  
Brain Tissue ◽  
Automated Quantification

Automated Quantification of Immunohistochemical Staining of Large Animal Brain Tissue Using QuPath Software

Neuroscience ◽  
2020 ◽  
Vol 429 ◽  
pp. 235-244 ◽  
Author(s):  
Nicholas J. Morriss ◽  
Grace M. Conley ◽  
Sara M. Ospina ◽  
William P Meehan III ◽  
Jianhua Qiu ◽  
...  
Keyword(s):  
Brain Tissue ◽  
Immunohistochemical Staining ◽  
Large Animal ◽  
Animal Brain ◽  
Automated Quantification

scholarly journals Automated Digital Quantification of Pulmonary Fibrosis in Human Histopathology Specimens

2021 ◽  
Vol 8 ◽  
Author(s):  
Lauren C. Testa ◽  
Yvon Jule ◽  
Linnea Lundh ◽  
Karine Bertotti ◽  
Melissa A. Merideth ◽  
...  
Keyword(s):  
Image Analysis ◽  
Pulmonary Fibrosis ◽  
Digital Image ◽  
Collagen Content ◽  
Serial Sections ◽  
Automated Quantification ◽  
Entire Lung ◽  
Lung Histopathology ◽  
Fibrotic Lung Disease ◽  
Picrosirius Red

Pulmonary fibrosis is characterized by abnormal interstitial extracellular matrix and cellular accumulations. Methods quantifying fibrosis severity in lung histopathology samples are semi-quantitative, subjective, and analyze only portions of sections. We sought to determine whether automated computerized imaging analysis shown to continuously measure fibrosis in mice could also be applied in human samples. A pilot study was conducted to analyze a small number of specimens from patients with Hermansky-Pudlak syndrome pulmonary fibrosis (HPSPF) or idiopathic pulmonary fibrosis (IPF). Digital images of entire lung histological serial sections stained with picrosirius red and alcian blue or anti-CD68 antibody were analyzed using dedicated software to automatically quantify fibrosis, collagen, and macrophage content. Automated fibrosis quantification based on parenchymal tissue density and fibrosis score measurements was compared to pulmonary function values or Ashcroft score. Automated fibrosis quantification of HPSPF lung explants was significantly higher than that of IPF lung explants or biopsies and was also significantly higher in IPF lung explants than in IPF biopsies. A high correlation coefficient was found between some automated quantification measurements and lung function values for the three sample groups. Automated quantification of collagen content in lung sections used for digital image analyses was similar in the three groups. CD68 immunolabeled cell measurements were significantly higher in HPSPF explants than in IPF biopsies. In conclusion, computerized image analysis provides access to accurate, reader-independent pulmonary fibrosis quantification in human histopathology samples. Fibrosis, collagen content, and immunostained cells can be automatically and individually quantified from serial sections. Robust automated digital image analysis of human lung samples enhances the available tools to quantify and study fibrotic lung disease.

scholarly journals Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 3050
Author(s):  
Jeppe Thagaard ◽  
Elisabeth Specht Stovgaard ◽  
Line Grove Vognsen ◽  
Søren Hauberg ◽  
Anders Dahl ◽  
...  
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Triple Negative ◽  
Tumor Infiltrating Lymphocytes ◽  
Cancer Type ◽  
Tumor Type ◽  
Breast Cancers ◽  
Automated Quantification ◽  
Infiltrating Lymphocytes

Triple-negative breast cancer (TNBC) is an aggressive and difficult-to-treat cancer type that represents approximately 15% of all breast cancers. Recently, stromal tumor-infiltrating lymphocytes (sTIL) resurfaced as a strong prognostic biomarker for overall survival (OS) for TNBC patients. Manual assessment has innate limitations that hinder clinical adoption, and the International Immuno-Oncology Biomarker Working Group (TIL-WG) has therefore envisioned that computational assessment of sTIL could overcome these limitations and recommended that any algorithm should follow the manual guidelines where appropriate. However, no existing studies capture all the concepts of the guideline or have shown the same prognostic evidence as manual assessment. In this study, we present a fully automated digital image analysis pipeline and demonstrate that our hematoxylin and eosin (H&E)-based pipeline can provide a quantitative and interpretable score that correlates with the manual pathologist-derived sTIL status, and importantly, can stratify a retrospective cohort into two significant distinct prognostic groups. We found our score to be prognostic for OS (HR: 0.81 CI: 0.72–0.92 p = 0.001) independent of age, tumor size, nodal status, and tumor type in statistical modeling. While prior studies have followed fragments of the TIL-WG guideline, our approach is the first to follow all complex aspects, where appropriate, supporting the TIL-WG vision of computational assessment of sTIL in the future clinical setting.

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