Blood vessel segmentation in en-face OCTA images: a frequency based method
This work addresses automated segmentation for improved analysis and diagnosis of retinal pathologies in medical imaging, but it is incremental as it applies an existing method (Gabor filters) to a new domain (OCTA images).
The paper tackled blood vessel segmentation in OCTA images by proposing a frequency-based method using Gabor filter banks, achieving results that aligned well with expert evaluations and automated device values, with qualitative feedback rated as very good and quantitative comparisons showing coincidence with manual annotations.
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.