SEMANTIC SEGMENTATION OF ORAL SQUAMOUS CELL CARCINOMA ON EPITHELLIAL AND STROMAL TISSUE

Abstract

Oral cancer (OC) is among the top ten cancers worlwide, with more than 90% being squamous cell carcinoma. Despite diagnostic and therapeutic development in OC patients’ mortality and morbidity rates remain high with no advancement in the last 50 years. Development of diagnostic tools in identifying pre-cancer lesions and detecting early-stage OC might contribute to minimal invasive treatment/surgery therapy, improving prognosis and survival rates, and maintaining a high quality of life of patients. For this reason, Artificial Intelligence (AI) algorithms are widely used as a computational aid in tumor classification and segmentation to help clinicians in the earlier discovery of cancer and better monitoring of oral lesions. In this paper, we propose an AI-based system for automatic segmentation of the epithelial and stromal tissue from oral histopathological images in order to assist clinicians in discovering new informative features. In terms of semantic segmentation, the proposed AI system based on preprocessing methods and deep convolutional neural networks produced satisfactory results, with 0.878 ± 0.027 mIOU and 0.955 ± 0.014 F1 score. The obtained results show that the proposed AI-based system has a great potential in diagnosing oral squamous cell carcinoma, therefore, this paper is the first step towards analysing the tumor microenvironment, specifically segmentation of the microenvironment cells.