Institute for Health Technologies, Nanyang Technological University, Singapore, Singapore.
SERI-NTU Advanced Ocular Engineering (STANCE), Singapore, Singapore.
Adv Exp Med Biol. 2020;1213:149-163. doi: 10.1007/978-3-030-33128-3_10.
The skin is the largest organ of our body. Skin disease abnormalities which occur within the skin layers are difficult to examine visually and often require biopsies to make a confirmation on a suspected condition. Such invasive methods are not well-accepted by children and women due to the possibility of scarring. Optical coherence tomography (OCT) is a non-invasive technique enabling in vivo examination of sub-surface skin tissue without the need for excision of tissue. However, one of the challenges in OCT imaging is the interpretation and analysis of OCT images. In this review, we discuss the various methodologies in skin layer segmentation and how it could potentially improve the management of skin diseases. We also present a review of works which use advanced machine learning techniques to achieve layers segmentation and detection of skin diseases. Lastly, current challenges in analysis and applications are also discussed.
皮肤是人体最大的器官。皮肤层内发生的皮肤疾病异常很难通过肉眼进行检查，通常需要进行活检以确认疑似病症。由于存在留疤的可能，这种有创方法不太被儿童和女性所接受。光学相干断层扫描（OCT）是一种非侵入性技术，无需切除组织即可对皮下皮肤组织进行活体检查。然而，OCT 成像中的一个挑战是 OCT 图像的解释和分析。在本综述中，我们讨论了皮肤层分割的各种方法，以及它如何可能改善皮肤病的管理。我们还介绍了使用先进的机器学习技术进行层分割和检测皮肤疾病的工作。最后，还讨论了分析和应用方面的当前挑战。
