A guide to human in vivo microcirculatory flow image analysis

Michael J Massey, Nathan I Shapiro, Michael J Massey, Nathan I Shapiro

Abstract

Various noninvasive microscopic camera technologies have been used to visualize the sublingual microcirculation in patients. We describe a comprehensive approach to bedside in vivo sublingual microcirculation video image capture and analysis techniques in the human clinical setting. We present a user perspective and guide suitable for clinical researchers and developers interested in the capture and analysis of sublingual microcirculatory flow videos. We review basic differences in the cameras, optics, light sources, operation, and digital image capture. We describe common techniques for image acquisition and discuss aspects of video data management, including data transfer, metadata, and database design and utilization to facilitate the image analysis pipeline. We outline image analysis techniques and reporting including video preprocessing and image quality evaluation. Finally, we propose a framework for future directions in the field of microcirculatory flow videomicroscopy acquisition and analysis. Although automated scoring systems have not been sufficiently robust for widespread clinical or research use to date, we discuss promising innovations that are driving new development.

Figures

Fig. 1
Fig. 1
SDF video analysis workflow chart. Raw video is captured by multiple study sites into cloud storage folders. A central processing facility syncs with the cloud storage and moves files to an onsite server for further processing. Videos are preprocessed to remove noise, resize, enhance contrast, and correct background illumination inhomogeneity. Videos are stabilized and clipped to specified duration and dimensions for analysis. Clips that cannot be stabilized over a minimum duration are discarded. Files may be organized by study folders. Clipped and stabilized videos are reviewed for image quality and assigned an image quality score. A quality sorting algorithm can be applied to select files for randomization and further analysis. Db database
Fig. 2
Fig. 2
An IQS form. File name includes metadata showing, for example, study site, patient ID, study name, date, timestamp, calibration, and preprocessing. Clicking the “Play” button opens the video file in a video player. The “Randomized” checkbox is not editable and indicates that the video has been assigned a random ID and selected for analysis. A quality score (0 = good, 1 = acceptable, or 10 = unacceptable) is assigned for each image quality category. Fields are provided to record specific time codes and comments

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