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Deep Convolutional Networks based on encoder-decoder architecture for automatic Optic Disc segmentation in retina images

Currently, deep convolutional neural networks (CNNs), are the latest methods applied to modern computer vision research technology. There are numerous architectures attributed to convolutional neural networks, based on deep learning methods applied to modern computer vision technology some of which are used for image segmentation; such as Fully Convolutional Networks (FCN), U-net, and LinkNet. In the scope of this study, we focused on the architectures of convolutional neural networks in particular. We present this architecture, and how it can be used for Image Segmentation. The novel model is presented to an automatic segmentation of the Optic Disc from retinal images. We use the Indian Diabetic Retinopathy Image Dataset (IDRiD) as dataset, our proposed CNN model based on an encoder-decoder model, this concept was adopted from the U-net architecture. The idea consists to apply other encoder-decoder levels in order to get more feature maps. The model was implemented with keras in python. We also implement other models such as FCN and U-net to compare their results. A brief description of the proposed model, training with data augmentation, validation and predictions are given. In the end we conclude that adding some levels on original architecture achieves a better result. Finally, an overview is given of future usage of convolutional neural networks for image segmentation problems in medical imaging contexts and the challenges involved therein.