Blood vessel segmentation in en-face OCTA images: a frequency based method

Anna Breger; Felix Goldbach; Bianca S. Gerendas; Ursula Schmidt-Erfurth; Martin Ehler

doi:10.1117/12.2611819

4 April 2022 Blood vessel segmentation in en-face OCTA images: a frequency based method

Anna Breger, Felix Goldbach, Bianca S. Gerendas, Ursula Schmidt-Erfurth, Martin Ehler

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 1203320 (2022) https://doi.org/10.1117/12.2611819
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Optical coherence tomography angiography (OCTA) is a novel noninvasive imaging modality for visualization of retinal blood flow in the human retina. Using specific OCTA imaging biomarkers for the identification of pathologies, automated image segmentations of the blood vessels can improve subsequent analysis and diagnosis. We present a novel method for the vessel density identification based on frequency representations of the image, in particular, using so-called Gabor filter banks. The algorithm is evaluated qualitatively and quantitatively on an OCTA image in-house data set from 10 eyes acquired by a Cirrus HD-OCT device. Qualitatively, the segmentation outcomes received very good visual evaluation feedback by experts. Quantitatively, we compared resulting vessel density values with the automated in-built values provided by the device. The results underline the visual evaluation. Furthermore, for the evaluation of the substep of FAZ identification manual annotations of 2 expert graders were used, showing that our results coincide well in visual and quantitative manners. Lastly, we suggest the computation of adaptive local vessel density maps that allow straightforward analysis of retinal blood flow in a local manner.

Citation Download Citation

Anna Breger, Felix Goldbach, Bianca S. Gerendas, Ursula Schmidt-Erfurth, and Martin Ehler "Blood vessel segmentation in en-face OCTA images: a frequency based method", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 1203320 (4 April 2022); https://doi.org/10.1117/12.2611819

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