Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Feb 16;4(3):100270.
doi: 10.1016/j.xjidi.2024.100270. eCollection 2024 May.

An Optimized and Advanced Algorithm for the Quantification of Immunohistochemical Biomarkers in Keratinocytes

Lindsey G Siegfried  1 , Sophie M Bilik  1 , Jamie L Burgess  1 , Paola Catanuto  1 , Ivan Jozic  1 , Irena Pastar  1 , Rivka C Stone  1 , Marjana Tomic-Canic  1
Affiliations

An Optimized and Advanced Algorithm for the Quantification of Immunohistochemical Biomarkers in Keratinocytes

Lindsey G Siegfried et al. JID Innov. .

Abstract

Advancements in pathology have given rise to software applications intended to minimize human error and improve efficacy of image analysis. Still, the subjectivity of image quantification performed manually and the limitations of the most ubiquitous tissue stain analysis software requiring parameters tuned by the observer, reveal the need for a highly accurate, automated nuclear quantification software specific to immunohistochemistry, with improved precision and efficiency compared with the methods currently in use. We present a method for the quantification of immunohistochemical biomarkers in keratinocyte nuclei proposed to overcome these limitations, contributing sensitive shape-focused segmentation, accurate nuclear detection, and automated device-independent color assessment, without observer-dependent analysis parameters.

Keywords: Automated digital pathology; Biomarker; Epidermal structures; Immunohistochemistry; Quantification.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Corresponding set of images from 5 DFUs analyzed by QuPath and SANDD to obtain total and positive nuclei counts. These images are referenced in Figure 10 and Table 2. Bar = 100 μm. DFU, diabetic foot ulcer; SANDD, Standardized Algorithm for Nuclear Diaminobenzidine Detection.
Figure 2
Figure 2
Results from positive cell detection function in QuPath. (a) QuPath interface with nuclei detected and user-defined settings for positive nuclei. (b) Portion of the region of interest showing uncounted nuclei (lacking a red or blue outline) as well as individual nuclei detected as multiple fragments. Bar = 100 μm.
Figure 3
Figure 3
Three-dimensional representation of the L∗a∗b∗ color space.
Figure 4
Figure 4
Flowchart demonstrating the steps of the SANDD process. IHC, immunohistochemistry; SANDD, Standardized Algorithm for Nuclear Diaminobenzidine Detection.
Figure 5
Figure 5
Image processing workflow in SANDD. (a) Original image of IHC-stained skin biopsy for phosphorylated glucocorticoid receptor. (b) Image after preprocessing in Photoshop to isolate epidermis. (c) Binary image after thresholding and segmentation. Nuclei are in white. (d) Binary image after thresholding and segmentation. Cytoplasm is in white. (e) Result of nucleus detection. Detections are outlined in green. (f) Final image output with positive nuclei outlined in red and negative nuclei outlined in blue. (g) Final image composited with original whole-section image. Bar = 100 μm. IHC, immunohistochemistry; SANDD, Standardized Algorithm for Nuclear Diaminobenzidine Detection.
Figure 6
Figure 6
Performance of QuPath versus SANDD in the region of interest fromFigure 1. (a) QuPath. (b) SANDD. QuPath fails to detect some nuclei (star) and splits single nuclei into fragments, with inconsistent detection of positive staining (red vs blue rim) in fragments from a single nucleus (arrows). Bar = 100 μm. SANDD, Standardized Algorithm for Nuclear Diaminobenzidine Detection.
Figure 7
Figure 7
SANDD application to murine skin stained for phosphorylated c-Jun. (a) Original image of IHC-stained porcine skin. (b) Isolated epidermal segment produced in Photoshop. (c) Final image showing negative nuclei outlined in blue and positive nuclei outlined in red. Bar = 4286 μm. IHC, immunohistochemistry; SANDD, Standardized Algorithm for Nuclear Diaminobenzidine Detection.
Figure 8
Figure 8
QuPath application to porcine skin IHC stained for phosphorylated c-Jun. (a) Original image of IHC-stained porcine skin. (b) Final image showing negative nuclei outlined in blue and positive nuclei outlined in red. Bar = 4180 μm. IHC, immunohistochemistry.
Figure 9
Figure 9
Qupath application to murine skin IHC stained for phosphorylated c-Jun. (a) Original image of IHC-stained murine skin. (b) Final image showing negative nuclei outlined in blue and positive nuclei outlined in red. Bar = 4286 μm. IHC, immunohistochemistry.
Figure 10
Figure 10
An improved method for quantification of nuclear biomarkers. (a) Comparison of QuPath with SANDD for percentage of positive keratinocytes stained for c-Myc in a set of 5 images, quantified by 4 observers. (b) Comparison of the coefficients of variation of quantifications (% positive nuclei) obtained by 4 observers analyzing images using QuPath versus SANDD. Data are expressed as mean ± SEM for n = 5 images. P = .008 using 2-tailed paired t-test. SANDD, Standardized Algorithm for Nuclear Diaminobenzidine Detection.
Figure 11
Figure 11
SANDD application to porcine skin IHC stained for phosphorylated c-Jun. (a) Original image of IHC-stained porcine skin. (b) Isolated epidermal segment produced in Photoshop. (c) Final image showing negative nuclei outlined in blue and positive nuclei outlined in red. Bar = 100 μm. IHC, immunohistochemistry; SANDD, Standardized Algorithm for Nuclear Diaminobenzidine Detection.
See this image and copyright information in PMC

References

    1. Akakin H, Gokozan H, Otero J, Gurcan M. An adaptive algorithm for detection of multiple-type, positively stained nuclei in IHC images with minimal prior information: application to OLIG2 staining gliomas. Paper presented at: SPIE Medical Imaging. 2015; Olando, FL.
    1. Anagnostou V.K., Welsh A.W., Giltnane J.M., Siddiqui S., Liceaga C., Gustavson M., et al. Analytic variability in immunohistochemistry biomarker studies. Cancer Epidemiol Biomarkers Prev. 2010;19:982–991. - PMC - PubMed
    1. Bankhead P., Loughrey M.B., Fernández J.A., Dombrowski Y., McArt D.G., Dunne P.D., et al. QuPath: open source software for digital pathology image analysis. Sci Rep. 2017;7 - PMC - PubMed
    1. Bradski G. The OpenCV Library. Dr Dobbs J. 2000;25:120–125.
    1. Chen L., Bao J., Huang Q., Sun H. A robust and automated cell counting method in quantification of digital breast cancer immunohistochemistry images. Pol J Pathol. 2019;70:162–173. - PubMed

LinkOut - more resources