Automatic differentiation of nonkeratinized stratified squamous epithelia and columnar epithelia through feature structure extraction using OCT. (July 2020)

• Record Type: Journal Article
• Authors: Xie, Jun; Chen, Si; Wang, Nanshuo; Wang, Lulu; Bo, En; Liu, Linbo
• Is Part Of: Biomedical signal processing and control. Volume 60(2020)
• Publication Date: 2020-07
• Subjects: OCT -- Metaplasia -- Feature extraction; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.101919
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: First time detecting metaplasia based on nucleus and basement membrane feature extraction in OCT imaging. First automatic detection and analysis algorithm of metaplasia studies. High accuracy, comparable with manual results by expert endoscopists. Non-invasive, high-resolution, highly automated and fast analysis of metaplasia lesion details and morphology. Abstract: As a type of precancerous lesion, metaplasia is usually considered to be associated with developing cancer. In clinical practice, surveillance of metaplastic cases usually relies on excisional biopsy followed by histological processing and analysis. As it is an invasive method accompanied by other complications, non-invasive imaging methods such as optical coherence tomography (OCT) can complement the existing method by enabling large area scanning. However, because it takes time to review large amount of data acquired from the whole suspected mucosal areas, an automatic classification method is preferred to alleviate the laboring hours and to avoid 'sampling errors' during image analysis. In this study, we report an automatic method to differentiate non-keratinized squamous epithelia and columnar epithelia in OCT images. A high detection accuracy is achieved by using feature structure extraction techniques in intact tissues.