EVA: Fully automatic hemodynamics assessment system for the bulbar conjunctival microvascular network. (April 2022)

• Record Type: Journal Article
• Authors: Yun, Zhaoqiang; Xu, Qing; Wang, Gengyuan; Jin, Shuang; Lin, Guoye; Feng, Qianjin; Yuan, Jin
• Is Part Of: Computer methods and programs in biomedicine. Volume 216(2022)
• Publication Date: 2022-04
• Subjects: Conjunctival microvasculature -- Hemodynamics -- Blood velocity measurement -- Image registration -- Vessel segmentation -- Image processing; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106631
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The proposed automated hemodynamic assessment system can improve the efficiency of conjunctival microvascular statistical analysis. An accurate blood velocity measurement algorithm based on line segment detection on a spatial-temporal image is proposed. A topology-preserving loss function is used to ensure the continuity of the segmentation results of the conjunctival microvascular, thereby improving the success rate of blood velocity measurement. Abstract: Background and objective: Conjunctival microcirculation has been used to quantitatively assess microvascular changes due to systemic disorders. The space between red blood cell clusters in conjunctival microvessels is essential for assessing hemodynamics. However, it causes discontinuities in vessel image segmentation and increases the difficulty of automatically measuring blood velocity. In this study, we developed an EVA system based on deep learning to maintain vessel segmentation continuity and automatically measure blood velocity. Methods: The EVA system sequentially performs image registration, vessel segmentation, diameter measurement, and blood velocity measurement on conjunctival images. A U-Net model optimized with a connectivity-preserving loss function was used to solve the problem of discontinuities in vessel segmentation. Then, an automatic measurement algorithm based on line segment detection was proposed to obtain accurate blood velocity. Finally, the EVA system assessed hemodynamic parameters based on the measured blood velocity in each vessel segment. Results: The EVA system was validated for 23 videos of conjunctival microcirculation captured using functional slit-lamp microscopy. The U-Net model produced the longest average vessel segment length, 158.03 ± 181.87 µm, followed by the adaptive threshold method and Frangi filtering, which produced lengths of 120.05 ± 151.47 µm and 99.94 ± 138.12 µm, respectively. The proposed method and one based on cross-correlation were validated to measure blood velocity for a dataset consisting of 30 vessel segments. Bland-Altman analysis showed that compared with the cross-correlation method (bias: 0.36, SD: 0.32), the results of the proposed method were more consistent with a manual measurement-based gold standard (bias: -0.04, SD: 0.14). Conclusions: The proposed EVA system provides an automatic and reliable solution for quantitative assessment of hemodynamics in conjunctival microvascular images, and potentially can be applied to hypoglossal microcirculation images.