Microvascular blood flow estimation in sublingual microcirculation videos based on a principal curve tracing algorithm

Microcirculatory perfusion is an important metric for diagnosing pathological conditions in patients. Capillary density and red blood cell (RBC) velocity provide a measure of tissue perfusion. Estimating RBC velocity is a challenging problem due to noisy video sequences, low contrast between the vessels and the background, and thousands of RBCs moving rapidly through video sequences. Typically, physicians manually trace small blood vessels and visually estimate RBC velocities. The task is labor intensive, tedious, and time-consuming. In this paper, we present a novel application of a principal curve tracing algorithm to automatically track RBCs across video frames and estimate their velocity based on the displacements of RBCs between two consecutive frames. The proposed method is implemented in one sublingual microcirculation video of a healthy subject.