Abstract

Microcirculatory monitoring plays an important role in diagnosis and treatment of critical care patients. Sidestream Dark Field (SDF) imaging devices have been used to visualize and support interpretation of the microvascular blood flflow. However, due to subsurface scattering within the tissue that embeds the capillaries, transparency of plasma, imaging noise and lack of features, it is diffifi- cult to obtain reliable physiological data from SDF videos. Therefore, thus far microcirculatory videos have been analyzed manually with signifificant input from expert clinicians. In this paper, we present a framework that automates the analysis process. It includes stages of video stabilization, enhancement, and micro-vessel extraction, in order to automatically estimate statistics of the micro blood flflows from SDF videos. Our method has been validated in critical care experiments conducted carefully to record the microcirculatory blood flflow in test animal subjects before, during and after induced bleeding episodes, as well as to study the effect of flfluid resuscitation. Our method is able to extract microcirculatory measurements that are consistent with clinical intuition and it has a potential to become a useful tool in critical care medicine