深度学习在皮肤OCT医学图像中的应用

Application of Deep Learning in OCT Medical Images of Skin

深度学习作为机器学习领域的重要研究方向,其强大的特征学习能力以及优秀的可移植性使得深度学习技术渗入到科学研究的各个领域,尤其是深度学习中前沿领域的成果对医学图像的识别、分类、分割、量化等方面起到了积极作用。光学相干断层扫描成像(OCT)技术具有非侵入、分辨率高的特点,能够无创获取生物组织内部微米级的高分辨率三维切面图像,在生物医学的眼科、血管、皮肤科等方面有着广泛应用。本文首先介绍了OCT及深度学习的基本原理以及二者相结合在皮肤领域的应用优势,然后详细阐述了深度学习在皮肤OCT医学图像领域的应用,主要包括在皮肤癌的分析分类、美容领域、指尖图像的分割以及在损伤皮肤的定性定量评估中的应用。

As an important research direction in the field of machine learning,deep learning technology has been infiltrated into various fields of scientific research due to its powerful feature of learning ability and excellent portability.In particular,the achievements in the frontier fields of deep learning have played a positive role in the recognition,classification,segmentation and quantification of medical images.Optical coherence tomography(OCT)technology has the characteristics of non-invasive,high resolution,non-invasive acquisition of micron-level high resolution 3D sectional image of biological tissue,and has been widely used in ophthalmology,vascular,dermatology and other biomedical fields.This paper first introduces the basic principles of OCT and deep learning and the advantages of their combination in the field of skin,and then elaborates the application of deep learning in the field of skin OCT medical images,including the analysis and classification of skin cancer,beauty,fingertip image segmentation,and the qualitative and quantitative evaluation of damaged skin.