Quantitative Morphometric Analysis of Oral Epithelial Dysplasia using Deep Learning-Based Nuclear Segmentation - Lacerda, Lucca S. P.; Ribeiro, Marcela F. A.; Horta, Martinho C. R.; Souto, Giovanna R.; Falcão, Alexandre X.; Rohde, Gustavo K.; Patrocínio Jr, Zenilton K. G.; Guimarães, Silvio Jamil F. (Anais Estendidos da Conference on Graphics, Patterns and Images (SIBGRAPI); 2025: Anais Estendidos da XXXVIII Conference on Graphics, Patterns and ImagesDO - 10.5753/sibgrapi.est.2025.38335, 2025)
Oral Epithelial Dysplasia (OED) is a histopathological diagnosis for potentially malignant lesions with a variable risk of malignant transformation. The conventional grading of OED relies on morphological assessment, a process known for significant inter-observer variability, which limits its prognostic reliability. This study leverages computational pathology to address this challenge by employing a deep learning-based tool, Cellpose, for the precise segmentation of epithelial cell nuclei. The objective was to facilitate the objective extraction and analysis of key nuclear morphometric features, such as area, perimeter, and compactness, from digitized histological images of OED. We utilized 30 H&E-stained images from a spectrum of diagnostic categories, including normal oral mucosa, dysplasias of varying grades, and oral squamous cell carcinoma (OSCC). The results demonstrated that quantitative features, particularly the variance in nuclear area (anisonucleosis), serve as a robust differentiator between diagnostic grades, with the most significant changes observed in OSCC. By providing a reproducible and quantitative framework, this deep learning-driven approach represents a significant step towards a standardized diagnostic support system for OED, with future potential for accurately predicting malignant transformation risk.