Boosted Exudate Segmentation in Retinal Images Using Residual Nets
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Exudates in retinal images are one of the early signs of the vision-threatening diabetic retinopathy and diabetic macular edema. Early diagnosis is very helpful in preventing the progression of the disease. In this work, we propose a fully automatic exudate segmentation method based on the state-of-the-art residual learning framework. With our proposed end-to-end architecture the training is done on small patches, but at the test time, the full sized segmentation is obtained at once. The small number of exudates in the training set and the presence of other bright regions are the limiting factors, which are tackled by our proposed importance sampling approach. This technique selects the misleading normal patches with a higher priority, and at the same time avoids the network to overfit to those samples. Thus, no additional post-processing is needed. The method was evaluated on three public datasets for both detecting and segmenting the exudates and outperformed the state-of-the-art techniques.
KeywordsExudate segmentation Retinal images Residual nets Importance sampling Diabetic retinopathy Diabetic macular edema
This project has received funding from the European Union’s Seventh Framework Programme, Marie Curie Actions-Initial Training Network, under Grant Agreement No. 607643, “Metric Analysis For Emergent Technologies (MAnET)". It was also supported by the Hé Programme of Innovation, which is partly financed by the Netherlands Organization for Scientific Research (NWO) under Grant No. 629.001.003.
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