Skip to main content
Journal cover image

Reproducibility of Retinal Vascular Phenotypes Obtained with Optical Coherence Tomography Angiography: Importance of Vessel Segmentation

Publication ,  Conference
Rashid, D; Cai, S; Giarratano, Y; Gray, C; Hamid, C; Grewal, DS; MacGillivray, T; Fekrat, S; Robbins, CB; Soundararajan, S; Ma, JP; Bernabeu, MO
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2021

Optical coherence tomography angiography (OCTA) is a non-invasive imaging method that can visualize the finest vascular networks in the human retina. OCTA image analysis has been successfully applied to the investigation of retinal vascular diseases of the eye and other systemic conditions that may manifest in the eye. To characterize and distinguish OCTA images from different pathologies, it is important to identify quantitative metrics and phenotypes that have high reproducibility and are not overly susceptible to the effects of imaging artifacts. This paper demonstrates the reproducibility of several recently demonstrated candidate OCTA quantitative metrics: mean curvature and tortuosity of the whole, foveal, superior, nasal, inferior, and temporal regions; foveal and parafoveal vessel skeleton density; and finally, foveal avascular zone area and acircularity index. This paper also highlights the importance of vessel segmentation choice on reproducibility using two different segmentation methods: optimally oriented flux and Frangi filter.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783030804312

Publication Date

January 1, 2021

Volume

12722 LNCS

Start / End Page

238 / 249

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Rashid, D., Cai, S., Giarratano, Y., Gray, C., Hamid, C., Grewal, D. S., … Bernabeu, M. O. (2021). Reproducibility of Retinal Vascular Phenotypes Obtained with Optical Coherence Tomography Angiography: Importance of Vessel Segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12722 LNCS, pp. 238–249). https://doi.org/10.1007/978-3-030-80432-9_19
Rashid, D., S. Cai, Y. Giarratano, C. Gray, C. Hamid, D. S. Grewal, T. MacGillivray, et al. “Reproducibility of Retinal Vascular Phenotypes Obtained with Optical Coherence Tomography Angiography: Importance of Vessel Segmentation.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12722 LNCS:238–49, 2021. https://doi.org/10.1007/978-3-030-80432-9_19.
Rashid D, Cai S, Giarratano Y, Gray C, Hamid C, Grewal DS, et al. Reproducibility of Retinal Vascular Phenotypes Obtained with Optical Coherence Tomography Angiography: Importance of Vessel Segmentation. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2021. p. 238–49.
Rashid, D., et al. “Reproducibility of Retinal Vascular Phenotypes Obtained with Optical Coherence Tomography Angiography: Importance of Vessel Segmentation.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12722 LNCS, 2021, pp. 238–49. Scopus, doi:10.1007/978-3-030-80432-9_19.
Rashid D, Cai S, Giarratano Y, Gray C, Hamid C, Grewal DS, MacGillivray T, Fekrat S, Robbins CB, Soundararajan S, Ma JP, Bernabeu MO. Reproducibility of Retinal Vascular Phenotypes Obtained with Optical Coherence Tomography Angiography: Importance of Vessel Segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2021. p. 238–249.
Journal cover image

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783030804312

Publication Date

January 1, 2021

Volume

12722 LNCS

Start / End Page

238 / 249

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences