27 February 2018 Lesion detection in ultra-wide field retinal images for diabetic retinopathy diagnosis
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Abstract
Diabetic retinopathy (DR) leads to irreversible vision loss. Diagnosis and staging of DR is usually based on the presence, number, location and type of retinal lesions. Ultra-wide field (UWF) digital scanning laser technology provides an opportunity for computer-aided DR lesion detection. High-resolution UWF images (3078×2702 pixels) may allow detection of more clinically relevant retinopathy in comparison with conventional retinal images as UWF imaging covers a 200° retinal area, versus 45° by conventional cameras. Current approaches to DR diagnosis that analyze 7-field Early Treatment Diabetic Retinopathy Study (ETDRS) retinal images provide similar results to UWF imaging. However, in 40% of cases, more retinopathy was found outside the 7- field ETDRS fields by UWF and in 10% of cases, retinopathy was reclassified as more severe. The reason is that UWF images examine both the central retina and more peripheral regions. We propose an algorithm for automatic detection and classification of DR lesions such as cotton wool spots, exudates, microaneurysms and haemorrhages in UWF images. The algorithm uses convolutional neural network (CNN) as a feature extractor and classifies the feature vectors extracted from colour-composite UWF images using a support vector machine (SVM). The main contribution includes detection of four types of DR lesions in the peripheral retina for diagnostic purposes. The evaluation dataset contains 146 UWF images. The proposed method for detection of DR lesion subtypes in UWF images using two scenarios for transfer learning achieved AUC ≈ 80%. Data was split at the patient level to validate the proposed algorithm.
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Anastasia Levenkova, Anastasia Levenkova, Arcot Sowmya, Arcot Sowmya, Michael Kalloniatis, Michael Kalloniatis, Angelica Ly, Angelica Ly, Arthur Ho, Arthur Ho, } "Lesion detection in ultra-wide field retinal images for diabetic retinopathy diagnosis", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057531 (27 February 2018); doi: 10.1117/12.2293434; https://doi.org/10.1117/12.2293434
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