Machine learning with a convolutional neural network for segmentation of ophthalmological images

Machine learning methods using convolution neural networks (CNN) are one technique which has played an important role in many biomedical applications. Here we apply the techniques to the detection of retinal blood vessels in ophthalmological images. Since the eye is one of the most important sensory organs in the human body, it is critical to diagnose diseases in the early stages. Early symptoms of various diseases like glaucoma, diabetic retinopathy, and cardiovascular diseases can be detected via the structure of the blood vessels of the retina. Studying the retinal blood vessel structures and network requires blood vessel segmentation. Deep learning has been used for the last five years and achieved state-of-the-art performance. More specifically, the latest development has been implemented to segment blood vessels more efficiently by using the u-net architecture. In the process, we used forward convolutional long short-term memory (convLSTM) to combine the feature map of the encoding path and the corresponding decoding path in lieu of simple concatenation in the skip connection of the u-net. We also used a connected convolution layer to the last layers of the encoding path to obtain more diverse features. The images used were from the DRIVE database and preprocessing was performed to obtain more accurate results. We achieved 95% accuracy, a precision of 91%, a sensitivity of 52%, and a specificity of 99%. Even with a low sensitivity of 52%, all the major blood vessels are found successfully.