Microaneurysm Localization in En Face Optical Coherence Tomography Angiography Images
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Abstract
A microaneurysm (MA) is a small, round outpouching of a capillary wall in the retina, typically caused by the weakening of the vessel due to diabetic retinopathy. Microaneurysms are generally difficult to detect because they are very small, low-contrast lesions that can be easily obscured by surrounding retinal structures or image noise. This study employed a machine learning method to localize clusters of MAs in en face Optical Coherence Tomography Angiography (OCTA) images. Twelve features were extracted from MA candidates identified through a rule-based method. A support vector machine was then used to filter out non-MA candidates. The density-based spatial clustering of applications with noise (DBSCAN) method was subsequently applied to localize the MA areas. A predicted location is considered correct if it lies within the ground truth MA area. We tested the method on 150 enface OCTA images known to contain MAs and compared it against the rule-based method. The proposed approach significantly improved the average recall of the rule-based method from 48.69% to 59.32%.
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