Abstract
Non-perfusion area (NPA) is a quantitative biomarker useful for characterizing ischemia in diabetic retinopathy (DR). Projection-resolved optical coherence tomographic angiography (PR-OCTA) allows visualization of retinal capillaries and quantify NPA in individual plexuses. However, poor scan quality can make current NPA detection algorithms unreliable and inaccurate. In this work, we present a robust NPA detection algorithm using convolutional neural network (CNN). By merging information from OCT angiograms and OCT reflectance images, the CNN could exclude signal reduction and motion artifacts and detect the avascular features from local to global with the resolution preserved. Across a wide range of signal strength indices, and on both healthy and DR eyes, the algorithm achieved high accuracy and repeatability.
Highlights
Diabetic retinopathy (DR) is a major cause of vision loss worldwide [1,2,3,4,5]
This can be qualified by the signal strength index (SSI), a proprietary measurement provided by averaging the logarithmic reflectance of tissue volume and normalizing the value in [0, 100] [27]
While Non-perfusion area (NPA) is as an important biomarker for DR [7,9,14,19], accurately quantifying NPA from optical coherence tomographic angiography (OCTA) scans can be challenging
Summary
Diabetic retinopathy (DR) is a major cause of vision loss worldwide [1,2,3,4,5]. Diabetic macular ischemia is an important clinical feature in DR [6,7,8,9], with prognostic value in DR [10,11,12]. Our recent progress on detecting NPA on 6 × 6mm SVC angiograms using a customized network [20,21] Despite this preliminary promise, it is more challenging to detect NPA in the ICP and DCP since these angiograms are more susceptible to signal reduction and motion artifacts. Projection artifacts were suppressed by our prior work, the discontinuity of vasculature caused by superficial vascular shadow is still very obvious, which makes the NPA detection more challenging. In this work we present a robust NPA detection algorithm using a CNN that, regardless of image quality, is able to detect NPA in three retinal plexuses by accurately distinguishing decreased flow signal from nonperfusion from artifacts caused by signal reduction and motion artifacts
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