Rashidi Mohammad, Kalenkov Georgy, Green Daniel J, McLaughlin Robert A
Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide SA 5005, Australia.
Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide SA 5005, Australia.
Biomed Opt Express. 2024 Sep 3;15(10):5592-5608. doi: 10.1364/BOE.525928. eCollection 2024 Oct 1.
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.
皮肤微血管系统对人类的心血管健康和体温调节至关重要，但其成像和分析面临重大挑战。已有的方法，如应用于光学相干断层扫描（OCT）B扫描进行OCT血管造影（OCTA）的散斑去相关，通常需要大量的B扫描，导致采集时间长且容易出现运动伪影。在我们的研究中，我们提出了一种在OCTA处理中集成深度学习算法的新方法。通过将卷积神经网络与挤压激励模块相结合，我们解决了微血管成像中的这些挑战。我们的方法通过有效利用局部信息提高了准确性并减少了测量时间。挤压激励模块通过动态重新校准特征进一步提高了稳定性和准确性，突出了深度学习在该领域的优势。
