August 2019
Volume 60, Issue 11
Open Access
ARVO Imaging in the Eye Conference Abstract  |   August 2019
Automatic 3D Vessel analysis framework for Optical Coherence Tomography Angiography (OCTA)
Author Affiliations & Notes
  • mona Sharifi Sarabi
    USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, California, United States
  • Jiong Zhang
    USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, California, United States
    Department of Ophthalmology, Keck School of Medicine of University of Southern California, USC Roski Eye Institute, Los Angeles, California, United States
  • Jin Kyu Gahm
    USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, California, United States
  • Amir Kashani
    Department of Ophthalmology, Keck School of Medicine of University of Southern California, USC Roski Eye Institute, Los Angeles, California, United States
  • Yonggang shi
    USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, California, United States
  • Footnotes
    Commercial Relationships   mona Sharifi Sarabi, None; Jiong Zhang, None; Jin Gahm, None; Amir Kashani, Carl Zeiss Meditec (R), Carl Zeiss Meditec (F); Yonggang shi, None
  • Footnotes
    Support  This work was supported by NIH grants UH3NS100614, R21EY027879, U01EY025864, K08EY027006, P41EB015922, P30EY029220, Research Equipment from Carl Zeiss Meditec and Unrestricted Departmental Grant from Research to Prevent Blindness.
Investigative Ophthalmology & Visual Science August 2019, Vol.60, 006. doi:
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    • Get Citation

      mona Sharifi Sarabi, Jiong Zhang, Jin Kyu Gahm, Amir Kashani, Yonggang shi; Automatic 3D Vessel analysis framework for Optical Coherence Tomography Angiography (OCTA). Invest. Ophthalmol. Vis. Sci. 2019;60(11):006.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : OCTA analysis typically involves 2D quantification based on the enface representation of retinal layers. 2D quantitative metrics can obscure the geometric and morphological information in 3D vasculature. There is a need for comprehensive 3D quantitative tools to more precisely model the complex dimensions of retinal vasculature available in OCTA scans. The aim of this study is to provide a robust and flexible 3D framework for the analysis of OCTA data.

Methods : We propose an automated 3D framework for an end-to-end analysis of OCTA images. Initially, OCTA retina region extracted using OCT explorer software. In the second step, 3D Fast Discrete Curvelet Transforms was used as a denoising and enhancement tool. Optimally Oriented Flux (OOF) was applied in a hybrid setting with curvelet to generate a clear vessel map. Binary vessel segmentation of OCTA volumes were then obtained by thresholding on OOF response. Ultimately, a 3D vessel skeleton was calculated by incorporating the Hamilton-Jacobian method. The proposed method is very flexible with respect to the selection of OCT layers and is not limited to only superficial or deep retina layers. Moreover, this method provides 3D analysis and quantification of all retina vessels and small capillaries that has the potential to be valuable in early diagnoses of DR.

Results : We validated our method on the OCTA images of a large-scale (n=338) DR study. Fig.1 demonstrates one representative eye selected from each group: Normal, Mild, Moderate, Severe, and PDR. For the quantitative assessment, 3D small vessel skeleton density (SVSD) of all eyes was extracted. The result of statistical analysis of healthy control compared to each stage of DR shows significant difference of SVSD in superficial and deep layers (p< 0.05). The test also reveals more significant difference (p< 0.001) in superficial layers for for earlier stage of DR (Mild), and our sublayer analysis localize the superficial layer alternation mainly in RNFL-GCL layer.

Conclusions : In this work we provide a robust, flexible and automatic 3D framework for analysis of retinal vasculature available in OCTA scans. The proposed method was tested on a large-scale Diabetic Retinopathy dataset using small vessel skeleton density (SVSD) feature. The quantitative assessment shows significant difference of SVSD in superficial and deep layers of each stage of DR compared to healthy control.

This abstract was presented at the 2019 ARVO Imaging in the Eye Conference, held in Vancouver, Canada, April 26-27, 2019.

 

Fig.1. Qualitative results

Fig.1. Qualitative results

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