A Neural Architecture Search Based Framework for Segmentation of Epithelium, Nuclei and Oral Epithelial Dysplasia Grading

Abstract

AbstractOral epithelial dysplasia (OED) is a pre-cancerous histopathological diagnosis given to a range of oral lesions. Architectural, cytological and histological features of OED can be modelled through the segmentation of full epithelium, individual nuclei and stroma (connective tissues) to provide significant diagnostic features. In this paper, we explore a customised neural architecture search (NAS) based method for optimisation of an efficient architecture for segmentation of the full epithelium and individual nuclei in pathology whole slide images (WSIs). Our initial experimental results show that the NAS-derived architecture achieves 93.5% F1-score for the full epithelium segmentation and 94.5% for nuclear segmentation outperforming other state-of-the-art models. Accurate nuclear segmentation allows us to perform quantitative statistical and morphometric feature analyses of the segmented nuclei within regions of interest (ROIs) of multi-gigapixel whole-slide images (WSIs). We show that a random forest model using these features can differentiate between low-risk and high-risk OED lesions.KeywordsOral epithelial dysplasiaComputational pathologyNeural architecture searchDigital pathologyDeep learningMachine learning