Validation of an automated method for studying retinal capillary blood flow.
Srividya NeriyanuriPhillip BedggoodR C Andrew SymonsAndrew B MethaPublished in: Biomedical optics express (2024)
Two major approaches for tracking cellular motion across a range of biological tissues are the manual labelling of cells, and automated analysis of spatiotemporal information represented in a kymograph. Here we compare these two approaches for the measurement of retinal capillary flow, a particularly noisy application due to the low intrinsic contrast of single red blood cells (erythrocytes). Image data were obtained using a flood-illuminated adaptive optics ophthalmoscope at 750 nm, allowing the acquisition of flow information over several cardiac cycles which provided key information in evaluating tracking accuracy. Our results show that in addition to being much faster, the automated method is more accurate in the face of rapid flow and reduced image contrast. This study represents the first validation of commonly used kymograph approaches to capillary flow analysis.
Keyphrases
- deep learning
- blood flow
- red blood cell
- magnetic resonance
- optical coherence tomography
- diabetic retinopathy
- health information
- machine learning
- induced apoptosis
- high throughput
- left ventricular
- healthcare
- cell cycle arrest
- heart failure
- photodynamic therapy
- high resolution
- computed tomography
- cell death
- mass spectrometry
- signaling pathway
- big data