title: Particle tracking for the assessment of microcirculatory perfusion.
abstract: In recent years the development of portable microscopes, which enable the noninvasive bedside evaluation of the sublingual microcirculation in critically ill patients, has expanded the clinical research on this level of the cardiovascular system. Several semi-quantitative scores have been defined in order to provide researchers with a standardized framework for the offline assessment of the microcirculation status. Among those, space-time diagrams (STDs) constitute an established method for obtaining an estimate of the red blood cells (RBCs) flow velocity in capillaries. However, STDs have the drawback of being time-consuming, inherently subjective, and difficult to manage when the flow is not regular.
  
  
  
  In this work we propose an automated method for calculating erythrocyte flow speed, aiming to provide a fast and objective tool for the evaluation of peripheral blood perfusion.
  
  
  
  The proposed method exploits an image segmentation module for estimating the positions of candidate flowing cells. A multi-object tracking algorithm based on Kalman filters analyzes and matches the positions corresponding to specific erythrocytes within consecutive frames. Thus, the output of the filter enables to estimate the displacement of each cell, yielding their instantaneous speed.
  
  
  
  The method has been validated against the results obtained by the manual analysis of STDs, proving a good agreement for speeds up to 300 μm s-1. At higher speeds, RBC tracking becomes unstable due to the currently limited video acquisition rate (25 Hz) of state-of-the-art devices, that makes the matching between objects appearing in consecutive frames very challenging.
authors.affiliation: Dept. of Information Engineering, University of Florence, Florence, Italy.
authors.forename: Michele
authors.initials: M
authors.lastname: Sorelli
authors.forename: Can
authors.initials: C
authors.lastname: Ince
authors.forename: Leonardo
authors.initials: L
authors.lastname: Bocchi
mesh_terms: D001783:Blood Flow Velocity; D004912:Erythrocytes / Q000166:cytology* / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008833:Microcirculation; D008853:Microscopy