Importance Of Stain Normalization In Enhancing Semantic Seg mentation Of Oral Squamous Cell Carcinoma Histopathological Images
DOI:
https://doi.org/10.48047/gcmbtz82Keywords:
OSCC, stain normalization, semantic segmentation, data augmentation, UNET.Abstract
In the field of histological image analysis for Oral Squamous Cell Carcinoma (OSCC), few studies have explored the integration of stain normalization methods as a preprocessing step in deep learning work flows. Standard Hematoxylin and Eosin (H&E) staining techniques are commonly used for tissue visualization, where Hematoxylin highlights cell nuclei in blue and Eosin colors the cytoplasm, muscle fibers, and other components in pink. Our research investigates the impact of applying Reinhard’s color transfer technique for stain normalization on the performance of deep learning models for semantic segmentation.
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Martino, F.; Bloisi, D.D.; Pennisi, A.; Fawakherji, M.; Ilardi, G.; Russo, D.; Nardi, D.; Staibano, S.; Merolla, F. Deep Learning-Based Pixel-Wise Lesion Segmentation on Oral Squamous Cell Carcinoma Images. Appl. Sci. 2020, 10, 8285. https://doi.org/10.3390/app10228285.
Pennisi, A.; Bloisi, D.D.; Nardi, D.; Varricchio, S. Multi-encoder U-Net for Oral Squamous Cell Carcinoma Image Segmentation. Available online: https://www.researchgate.net/publication/362864776 (accessed on 20 January 2025).
Musulin, J.; Štifanić, D.; Zulijani, A.; Čabov, T.; Dekanić, A.; Car, Z. An Enhanced Histopathology Analysis: An AI-Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue. Cancers 2021, 13, 1784. https://doi.org/10.3390/cancers13081784.
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