In Silico Modelling of Blood Vessel Segmentations for Estimation of Discretization Error in Spatial Measurement and its Impact on Quantitative Fluorescence Angiography

Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:4787-4790. doi: 10.1109/EMBC.2019.8857146.


Today the vascular function after interventions as Bypass surgeries are checked qualitatively by observing the blood dynamics inside the vessel via Indocyanine Green (ICG) Fluorescence Angiography. This state-of-the-art should be upgraded and has to be improved and converted towards a quantitatively measured blood flow. Previous approaches show that the blood flow measured from fluorescence angiography cannot be easily calibrated to a gold standard reference. In order to systematically address the possible source of error we investigate as a first step the discretization error in a camera-based measurement of the vessel's geometry. In order to generate an error-free ground truth, a vessel model has been developed based on mathematical functions. This database is then used to determine the error in discretizing the centerline of the structure and estimate its effects on the accuracy of the flow calculation. As result the model is implemented according to the conditions which are set up to ensure transferability on camera-based segmentations of vessels. In this paper the relative discretization error for estimating the centerline length of segmented vessels could be calculated in the range of 6.3%. This would reveal significant error propagated to the estimation of the blood flow value derived by camera-based angiography.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Computer Simulation*
  • Fluorescein Angiography
  • Humans
  • Indocyanine Green*
  • Vascular Surgical Procedures*


  • Indocyanine Green