Optical coherence tomography (OCT) has been used for visualization of morphological change of tissues over time. Although current OCT technology allows the volumetric and high throughput information of tissues, its quantification and analysis still uses time inefficient and tedious process. In order to fully utilize benefits of OCT, it is desired to integrate the intelligent software platform. As deep learning technology is advanced, it has been emerged as the alternative way for quantitative and automated image processing in bio-imaging field including optical imaging. Deep leaning technique is based on the sufficient training data which could overcome the drawback of traditional handcrafted optical image processing algorithms.
In this study, we introduce a novel and intelligent OCT software platform for accurate skin analysis and classification using deep learning module. Our platform is equipped with automated calculations of morphological skin parameters, such as surface roughness, wrinkle depth, volume, and epidermal thickness. To date, most promising tool for quantitative skin analysis is to use a software package of PRIMOS device which relies on three-dimensional camera systems. In order to evaluate our software platform, we compared OCT skin parameters based on deep learning technique and conventional PRIMOS data. Our preliminary study shows that proposed software platform for 3D OCT is a promising tool for accurate, efficient, and quantitative analysis of volumetric skin. It could be also a better alternative than existing PRIMOS solutions to both cosmeceutical and dermatological field.
Sanzhar Askaruly, Yujin Ahn, Jiho Bak, Andrey Vavilin, Gil-Jin Jang, Pilun Kim, Haekwang Lee, and Woonggyu Jung, "Quantitative classification of OCT skin images with deep learning (Conference Presentation)," Proc. SPIE 10467, Photonics in Dermatology and Plastic Surgery 2018, 1046709 (Presented at SPIE BiOS: January 27, 2018; Published: 14 March 2018); https://doi.org/10.1117/12.2288378.5751326338001.
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