Abstract
Skin microvasculature is essential for cardiovascular health and thermoregulation in humans, yet its imaging and analysis pose significant challenges. Established methods, such as speckle decorrelation applied to optical coherence tomography (OCT) B-scans for OCT-angiography (OCTA), often require a high number of B-scans, leading to long acquisition times that are prone to motion artifacts. In our study, we propose a novel approach integrating a deep learning algorithm within our OCTA processing. By integrating a convolutional neural network with a squeeze-and-excitation block, we address these challenges in microvascular imaging. Our method enhances accuracy and reduces measurement time by efficiently utilizing local information. The Squeeze-and-Excitation block further improves stability and accuracy by dynamically recalibrating features, highlighting the advantages of deep learning in this domain.