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Open Access Research article

An automated method for analysis of microcirculation videos for accurate assessment of tissue perfusion

Sumeyra U Demir1, Roya Hakimzadeh1, Rosalyn Hobson Hargraves124, Kevin R Ward1456, Eric V Myer1 and Kayvan Najarian1345*

Author Affiliations

1 Signal Processing Technologies LLC, Richmond, VA, USA

2 Department of Electrical Engineering, Virginia Commonwealth University, Richmond, VA, USA

3 Department of Emergency Medicine, Virginia Commonwealth University, Richmond, VA, USA

4 Virginia Commonwealth University Reanimation Engineering Science (VCURES), Richmond, VA, USA

5 Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA

6 Department of Emergency Medicine, Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI, USA

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BMC Medical Imaging 2012, 12:37  doi:10.1186/1471-2342-12-37

Published: 21 December 2012

Abstract

Background

Imaging of the human microcirculation in real-time has the potential to detect injuries and illnesses that disturb the microcirculation at earlier stages and may improve the efficacy of resuscitation. Despite advanced imaging techniques to monitor the microcirculation, there are currently no tools for the near real-time analysis of the videos produced by these imaging systems. An automated system tool that can extract microvasculature information and monitor changes in tissue perfusion quantitatively might be invaluable as a diagnostic and therapeutic endpoint for resuscitation.

Methods

The experimental algorithm automatically extracts microvascular network and quantitatively measures changes in the microcirculation. There are two main parts in the algorithm: video processing and vessel segmentation. Microcirculatory videos are first stabilized in a video processing step to remove motion artifacts. In the vessel segmentation process, the microvascular network is extracted using multiple level thresholding and pixel verification techniques. Threshold levels are selected using histogram information of a set of training video recordings. Pixel-by-pixel differences are calculated throughout the frames to identify active blood vessels and capillaries with flow.

Results

Sublingual microcirculatory videos are recorded from anesthetized swine at baseline and during hemorrhage using a hand-held Side-stream Dark Field (SDF) imaging device to track changes in the microvasculature during hemorrhage. Automatically segmented vessels in the recordings are analyzed visually and the functional capillary density (FCD) values calculated by the algorithm are compared for both health baseline and hemorrhagic conditions. These results were compared to independently made FCD measurements using a well-known semi-automated method. Results of the fully automated algorithm demonstrated a significant decrease of FCD values. Similar, but more variable FCD values were calculated using a commercially available software program requiring manual editing.

Conclusions

An entirely automated system for analyzing microcirculation videos to reduce human interaction and computation time is developed. The algorithm successfully stabilizes video recordings, segments blood vessels, identifies vessels without flow and calculates FCD in a fully automated process. The automated process provides an equal or better separation between healthy and hemorrhagic FCD values compared to currently available semi-automatic techniques. The proposed method shows promise for the quantitative measurement of changes occurring in microcirculation during injury.