Blood flow characterization in nailfold capillary using optical flow-assisted two-stream network and spatial-temporal image

Abstract

The blood flow velocity in the nailfold capillary is an important indicator of the status of microcirculation. The conventional manual processing method is both laborious and prone to human artifacts. A feasible way to solve this problem is to use machine learning to assist in image processing and diagnosis. Inspired by the Two-Stream Convolutional Networks, this study proposes an optical flow-assisted two-stream network to segment nailfold blood vessels. Firstly, we use U-Net as the spatial flow network and the dense optical flow as the temporal stream. The results show that the optical flow information can effectively improve the integrity of the segmentation of blood vessels. The overall accuracy is 94.01 %, the Dice score is 0.8099, the IoU score is 0.6806, and the VOE score is 0.3194. Secondly, The flow velocity of the segmented blood vessel is determined by constructing the spatial-temporal (ST) image. The blood flow velocity evaluated is consistent with the typical blood flow speed reported. This study proposes a novel two-stream network for blood vessel segmentation of nailfold capillary images. Combined with ST image and line detection method, it provides an effective workflow for measuring the blood flow velocity of nailfold capillaries.