Abstract

Optical coherence tomography angiography (OCTA) is a major advancement in imaging, offering high-resolution microvascular volumetric images crucial for diagnosing and studying dermatological diseases. However, current data analysis and clinical evaluation criteria primarily rely on 2-dimensional (2D) imaging results, resulting in imprecise diagnoses due to the substantial loss of 3D curved structures and microvascular details. To address this issue, we propose a high-fidelity 3D curved processing workflow that integrates an artificial neural network (ANN) with a 3D denoising algorithm based on the curvelet transform and optimal orientation flow (OOF). This innovative workflow enables precise 3D segmentation and accurate quantification of dermal layer microvasculature in atopic dermatitis (AD) in vivo. Furthermore, the use of 3D multiparametric microvasculature quantitative metrics establishes a robust framework for assessing the efficacy of AD treatments in 3D images. Our study results demonstrate that skin structure imaging and the dynamic evolution of 3D microvasculature align with observed pathological changes. Compared to traditional 2D analysis, the maximum variation rate of 3D curved multiparametric information is approximately 10%. Consequently, our research marks a significant advancement in the accurate quantification of microvasculature in AD development and theranostics, paving the way for the clinical application of OCTA in dermatology.

Link：https://spj.science.org/doi/10.34133/research.0778