Diabetic retinopathy lesion segmentation using deep multi-scale framework

Abstract

The segmentation of diabetic retinopathy (DR) lesions is important for large-scale screening using color fundus photography (CFP) images. The difficulty of this task is that the DR lesions have various sizes, shapes, and intensities. Traditional handcrafted feature-based approaches are unsatisfactory, and recent deep-learning-based approaches ignore the features of DR lesions. In this paper, we propose a segmentation approach that segments four typical DR lesions simultaneously based on convolutional neural networks (CNN). The raw CFP image is first pre-processed and resized to different sizes. A set of fully convolutional neural networks (FCN) with various input sizes are then trained to extract the lesions from different scales. An auxiliary CNN is then introduced to fuse the output from these FCN and refine the segmentation result. We conducted our experiments on one local dataset and four public datasets including IDRiD, DDR, E-ophtha, and DIARETDB1. The results of the area under the precision–recall-curve (AUPR) and the dice similarity coefficient (DSC) show that our approach achieves competitive performance. The improvement in performance indicates that this approach is beneficial to DR lesion segmentation and has potential in other segmentation tasks.