Department of Mathematics and Informatics, University of Palermo, 90128 Palermo, Italy.
Department of Physics and Chemistry, University of Palermo, 90128 Palermo, Italy.
Sensors (Basel). 2023 Sep 5;23(18):7674. doi: 10.3390/s23187674.
Oral capillaroscopy is a critical and non-invasive technique used to evaluate microcirculation. Its ability to observe small vessels in vivo has generated significant interest in the field. Capillaroscopy serves as an essential tool for diagnosing and prognosing various pathologies, with anatomic-pathological lesions playing a crucial role in their progression. Despite its importance, the utilization of videocapillaroscopy in the oral cavity encounters limitations due to the acquisition setup, encompassing spatial and temporal resolutions of the video camera, objective magnification, and physical probe dimensions. Moreover, the operator's influence during the acquisition process, particularly how the probe is maneuvered, further affects its effectiveness. This study aims to address these challenges and improve data reliability by developing a computerized support system for microcirculation analysis. The designed system performs stabilization, enhancement and automatic segmentation of capillaries in oral mucosal video sequences. The stabilization phase was performed by means of a method based on the coupling of seed points in a classification process. The enhancement process implemented was based on the temporal analysis of the capillaroscopic frames. Finally, an automatic segmentation phase of the capillaries was implemented with the additional objective of quantitatively assessing the signal improvement achieved through the developed techniques. Specifically, transfer learning of the renowned U-net deep network was implemented for this purpose. The proposed method underwent testing on a database with ground truth obtained from expert manual segmentation. The obtained results demonstrate an achieved Jaccard index of 90.1% and an accuracy of 96.2%, highlighting the effectiveness of the developed techniques in oral capillaroscopy. In conclusion, these promising outcomes encourage the utilization of this method to assist in the diagnosis and monitoring of conditions that impact microcirculation, such as rheumatologic or cardiovascular disorders.
口腔毛细血管镜检查是一种用于评估微循环的关键且非侵入性技术。它能够在体内观察到小血管，这一特性引起了该领域的极大兴趣。毛细血管镜检查是诊断和预测各种病理的重要工具，解剖病理学病变在其进展中起着关键作用。尽管其重要性不言而喻，但由于采集设置的限制，包括视频摄像机的空间和时间分辨率、客观放大倍数和物理探头尺寸，口腔内的视频毛细血管镜检查的应用受到了限制。此外，操作人员在采集过程中的影响，特别是探头的操纵方式，进一步影响了其效果。本研究旨在通过开发一种用于微循环分析的计算机支持系统来应对这些挑战并提高数据可靠性。该系统旨在对口腔黏膜视频序列中的毛细血管进行稳定化、增强和自动分割。稳定化阶段是通过在分类过程中使用基于种子点的方法来完成的。所实现的增强过程是基于毛细血管镜图像的时间分析。最后，实现了毛细血管的自动分割阶段，并且还额外实现了定量评估通过所开发技术获得的信号改善的目标。具体来说，为此目的实施了著名的 U-net 深度网络的迁移学习。该方法在具有从专家手动分割获得的真实数据的数据库上进行了测试。获得的结果表明实现了 90.1%的 Jaccard 指数和 96.2%的准确性，突出了所开发技术在口腔毛细血管镜检查中的有效性。总之，这些有前途的结果鼓励将这种方法用于辅助诊断和监测影响微循环的疾病，如风湿性或心血管疾病。
