An automatic retinal vessel segmentation approach based on Convolutional Neural Networks

The image segmentation concept is a mechanism of image processing that allows cutting an image into several image sections or parts. Numerous techniques are applied to achieve image segmentation, such as color, pixel value, deep Convolutional Neural Networks, and others. We are interested in Convolutional Neural Network (CNN) based approaches. In this paper, we present an automatic method for blood vessel segmentation in retina images; we propose the use of deep CNNs to achieve this goal. Our proposed model is a multi-encoder decoder architecture composed of two encoder units with convolutional and maxpoling layers and a decoder unit with convolutional and deconvolutional layers; it takes an RGB retina image directly as input. The model, then, is trained to build feature maps from convolution, deconvolution operations and nonlinear activation function in order to reconstruct the feature map and ultimately obtain the output layer as segmentation of retina vessels. Structured retina analysis (STARE) and Digital images of the retina for vessel extraction (DRIVE) are used as datasets for training and evaluation. We implement the model with keras in python. An overview of the model architecture, training with data augmentation techniques, validation and prediction is presented. The results are compared to existing labeled data; and the performance of our model with different metrics like F1 score, accuracy, sensitivity, specificity and precision is evaluated. The results obtained for the different metrics listed above are respectively 0.8321, 0.9716, 0.8214, 0.9860 and 0.8466. In addition, the results of our work are submitted into Drive Challenge in order to get an external evaluation based on dice coefficient calculation with the dice-mean score being 0.8283. The results show that our method is, to a great extent, much better than other CNN-based methods, with a precision of 0.9707 for DRIVE datasets versus 0.9568 and 0.9469 for the others. The results obtained from our proposed model indicate that our method has a promising potential for practical applications like computer analysis of retinal images such as automated screening for precocious detection of diabetic retinopathy. These results are due to the model's segmentation performance, and, in particular, its enhanced specificity, accuracy and precision.