Automated capillary flow segmentation and mapping for nailfold video capillaroscopy

• Published in: Microcirculation Vol. 29; no. 3; pp. e12753 - n/a
• Main Authors: Niizawa, Tomoya, Yokemura, Kota, Kusaka, Tomoya, Sugashi, Takuma, Miura, Ichiro, Kawagoe, Keiji, Masamoto, Kazuto
• Format: Journal Article
• Language: English
• Published: United States Wiley 01.04.2022; Wiley Subscription Services, Inc
• Subjects: Aged; aging; Automation; Capillaries; Capillaries - diagnostic imaging; health care; hemorheology; Humans; image analysis; kymograph; Microscopic Angioscopy; Microscopic Angioscopy - methods; Middle Aged; Nails; Nails - blood supply; Nails - diagnostic imaging; Spectrum analysis; Veins; hemorheology; aging; kymograph; image analysis; health care
• DOI: 10.1111/micc.12753
• ISSN: 1073-9688; 1549-8719

Objective This study aimed to develop an automated image analysis method for segmentation and mapping of capillary flow dynamics captured using nailfold video capillaroscopy (NVC). Methods were applied to compare capillary flow structures and dynamics between young and middle‐aged healthy controls. Methods NVC images were obtained in a resting state, and a region of the vessel in the image was extracted using a conventional U‐Net neural network. The approximate length, diameter, and radius of the curvature were calculated automatically. Flow speed and its fluctuation over time were mapped using the Radon transform and frequency spectrum analysis from the kymograph image created along the vessel's centerline. Results The diameter of the curve segment (14.4 μm and 13.0 μm) and the interval of two straight segments (13.7 μm and 32.1 μm) of young and middle‐aged subjects, respectively, were significantly different. Faster flow was observed in older subjects (0.48 mm/s) than in younger subjects (0.26 mm/s). The power spectral analysis revealed a significant correlation between the high‐frequency power spectrum and the flow speed. Conclusions The present method allows a spatiotemporal characterization of capillary morphology and flow dynamics with NVC, allowing a wide application such as large‐scale health assessment.