Automated Erythrocyte Detection and Tracking for Retinal Blood Flow Quantification in Erythrocyte-Mediated Angiography
2026-05-31 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors developed a new computer method called EMTrack to help measure blood flow in the tiny blood vessels of the eye by tracking red blood cells. They created a special technique to better spot moving blood cells and keep track of them even when they move a lot. They also made a new dataset with lots of examples to train and test their method. Their results show that EMTrack works better than previous methods for detecting and following red blood cells, which can help measure eye blood flow automatically.
retinal blood flowerythrocyte-mediated angiographyred blood cellscomputer visioncell trackingbiomedical imagingdataset annotationflow-context modulecapillary-level measurement
Authors
Chiao-Yi Wang, Havish S Gadde, Yi-Ting Shen, Saige M. Oechsli, Osamah Saeedi, Yang Tao
Abstract
Capillary-level retinal blood flow (RBF) has strong potential as a biomarker for various ocular diseases. However, modalities for measuring capillary-level RBF remain limited. Erythrocyte-mediated angiography (EMA), an emerging imaging technique, enables capillary-level RBF measurement by visualizing individual erythrocytes, yet automated erythrocyte detection and tracking, which are essential for quantifying blood flow, remain largely unexplored. To address this gap, we propose EMTrack, a novel framework featuring a flow-context module for erythrocyte detection that distinguishes moving from paused cells and a topology-aware tracking strategy that enables tracking under large inter-frame displacements and substantial motion variations. In addition, we establish RBF-EMA, a new EMA dataset with comprehensive erythrocyte detection and tracking annotations. Experimental results demonstrate that our method outperforms baseline methods both quantitatively and qualitatively on detection and tracking tasks in the RBF-EMA dataset. Moreover, RBF quantification results highlight the strong potential of our framework for automated retinal blood flow measurement.