Rapid automatic assessment of microvascular density in sidestream dark field images

Abstract

The purpose of this study was to develop a rapid and fully automatic method for the assessment of microvascular density and perfusion in sidestream dark field (SDF) images. We modified algorithms previously developed by our group for microvascular density assessment and introduced a new method for microvascular perfusion assessment. To validate the new algorithm for microvascular density assessment, we reanalyzed a selection of SDF video clips (n = 325) from a study in intensive care patients and compared the results to (semi-)manually found microvascular densities. The method for microvascular perfusion assessment (temporal SDF image contrast analysis, tSICA) was tested in several video simulations and in one high quality SDF video clip where the microcirculation was imaged before and during circulatory arrest in a cardiac surgery patient. We found that the new method for microvascular density assessment was very rapid (<30 s/clip) and correlated excellently with (semi-)manually measured microvascular density. The new method for microvascular perfusion assessment (tSICA) was shown to be limited by high cell densities and velocities, which severely impedes the applicability of this method in real SDF images. Hence, here we present a validated method for rapid and fully automatic assessment of microvascular density in SDF images. The new method was shown to be much faster than the conventional (semi-)manual method. Due to current SDF imaging hardware limitations, we were not able to automatically detect microvascular perfusion.