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

TL;DR

This paper introduces a novel frequency-based segmentation method using Gabor filter banks for analyzing retinal blood vessels in OCTA images, demonstrating good qualitative and quantitative results on in-house data.

Contribution

The paper presents a new segmentation approach based on frequency representations, specifically Gabor filters, for improved retinal vessel analysis in OCTA images.

Findings

Segmentation outcomes received very good expert visual feedback.

Quantitative vessel density values closely matched device-provided values.

Manual annotations showed high agreement with the proposed method.

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 segmentation method for 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 the resulting vessel density values with automated in-built values provided by the device. The results underline the visual evaluation. For the evaluation of the FAZ identification substep, 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.