Automatic segmentation of retinal edema in optical coherence tomography based on deep neural networks

A multi-task network framework based on convolutional neural networks improvements is proposed for automatic segmentation and classification in three different lesion areas of retinal OCT images. We propose a dilated convolutions module to capture fine-grained semantic information and introduce dense skip connections to integrate features of multiple scales and different semantic levels. Furthermore, we use a joint loss function that balances the relative size of the data labels and the improved generalization of the network. The validity and reliability of our proposed method are trained and verified on OCT datasets. Extensive experimental results demonstrate that the average Dice coefficient of the three lesion areas is 2.61% higher than the most commonly used U-net model in the field of medical image segmentation and reaches 0.81501.