EpidermaQuant: Unsupervised Detection and Quantification of Epidermal Differentiation Markers on H-DAB-Stained Images of Reconstructed Human Epidermis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data
2.2. Color Normalization and Deconvolution
2.3. Background Detection and Mask Creation
2.4. Image Rotation and Crop
- The tissue mask (negation of background mask) is used as the input;
- The mask is rotated from 1 to 180 degrees with a step of 0.1 degrees;
- For each rotation position, the sum of pixels in each row of the mask is calculated to create boundary intensity distribution;
- For each rotation position, the maximum value of the boundary intensity distribution is found;
- The final rotation position is the one that maximizes the maximum value of the boundary intensity.
2.5. Elimination of Images without DAB Stain
2.6. DAB Region Segmentation
2.7. EpidermaQuant Framework
3. Results
3.1. Color Normalization and Deconvolution Results
3.2. Background Removal and Image Rotation
3.3. Elimination of Images without DAB Stain Results
3.4. DAB Area Detection
3.5. Comparison of DAB-Staining Levels between Markers
3.6. Validation of Developed Pipeline on Human Protein Atlas Data
3.7. Ablation Study
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Zamojski, D.; Gogler, A.; Scieglinska, D.; Marczyk, M. EpidermaQuant: Unsupervised Detection and Quantification of Epidermal Differentiation Markers on H-DAB-Stained Images of Reconstructed Human Epidermis. Diagnostics 2024, 14, 1904. https://doi.org/10.3390/diagnostics14171904
Zamojski D, Gogler A, Scieglinska D, Marczyk M. EpidermaQuant: Unsupervised Detection and Quantification of Epidermal Differentiation Markers on H-DAB-Stained Images of Reconstructed Human Epidermis. Diagnostics. 2024; 14(17):1904. https://doi.org/10.3390/diagnostics14171904
Chicago/Turabian StyleZamojski, Dawid, Agnieszka Gogler, Dorota Scieglinska, and Michal Marczyk. 2024. "EpidermaQuant: Unsupervised Detection and Quantification of Epidermal Differentiation Markers on H-DAB-Stained Images of Reconstructed Human Epidermis" Diagnostics 14, no. 17: 1904. https://doi.org/10.3390/diagnostics14171904