OCTAVA: An open-source toolbox for quantitative analysis of optical coherence tomography angiography images

PLoS One. 2021 Dec 9;16(12):e0261052. doi: 10.1371/journal.pone.0261052. eCollection 2021.

Abstract

Optical coherence tomography angiography (OCTA) performs non-invasive visualization and characterization of microvasculature in research and clinical applications mainly in ophthalmology and dermatology. A wide variety of instruments, imaging protocols, processing methods and metrics have been used to describe the microvasculature, such that comparing different study outcomes is currently not feasible. With the goal of contributing to standardization of OCTA data analysis, we report a user-friendly, open-source toolbox, OCTAVA (OCTA Vascular Analyzer), to automate the pre-processing, segmentation, and quantitative analysis of en face OCTA maximum intensity projection images in a standardized workflow. We present each analysis step, including optimization of filtering and choice of segmentation algorithm, and definition of metrics. We perform quantitative analysis of OCTA images from different commercial and non-commercial instruments and samples and show OCTAVA can accurately and reproducibly determine metrics for characterization of microvasculature. Wide adoption could enable studies and aggregation of data on a scale sufficient to develop reliable microvascular biomarkers for early detection, and to guide treatment, of microvascular disease.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Algorithms*
  • Forearm / blood supply
  • Forearm / diagnostic imaging*
  • Hand / blood supply
  • Hand / diagnostic imaging*
  • Healthy Volunteers
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Microvessels / diagnostic imaging*
  • Middle Aged
  • Signal-To-Noise Ratio
  • Tomography, Optical Coherence / methods*

Grants and funding

GRU received funding from the IPRS (University of Western Australia) and the Rank Prize Covid-19 response fund. RSM and AKD are supported by the Doctoral College studentship award (University of Surrey). CH and DMS received funding from the Higher Education Innovation Funding, Industrial Strategy Internal Funding (University of Surrey). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.