Analysis of microcirculation is crucial for several clinical outcomes. Heterogeneity of blood flow and low perfusion in microcirculation is linked to outcomes in sepsis, cardiogenic shock and hemodynamically compromised patients. Although imaging advancements have progressed substantially in the last couple of decades, manual analysis of microcirculation remains the gold standard. Capillary detection in microcirculation videos is difficult because of sublingual or hand motion in the handheld videos, and intermittent visibility of capillaries due to plasma gaps. Most automated methods perform capillary segmentation on a mean-image over time, which fail to detect intermittently visible capillaries due to their weaker contrast as a result of averaging. To normalize the spatial contrast in capillaries, we propose to use a relative perfusion feature map. Our feature map can be used in both unsupervised and supervised settings. Experiments show that our method leads to consistent improvements in total vessel density and centerline recall over baselines. Statistically, our method has lesser bias, achieves better agreement with the ground truth, and correlates better with manual annotations, making it a clinically viable benchmark.
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