RetU-Net: An Enhanced U-Net Architecture for Retinal Lesion Segmentation

Abstract

Diabetic retinopathy is a predominant vision-threatening disease affecting working-aged people specifically. Timely diagnosis through early detection and prevention helps to reduce the risk of severe vision loss. Computer-aided diagnosis in retinal image analysis through Machine Learning techniques will help medical professionals perform their analysis better. Automated image processing through Convolutional Neural Networks has proven to be a promising technique, mainly in medical image segmentation. Convolutional Neural Network techniques like 3D CNN, Deep CNN and architectures like U-Net, V-Net, SegNet, and DeepMedic have outperformed medical image analysis results. However, the baseline CNN architectures struggle to retain high-quality information at the output and thus affect the performance. This work focuses on addressing the issue of translational variance and overfitting scenarios of U-Net architecture by experimenting with adaptable window sizes, pretrained weights and linear interpolation technique. A novel U-Net based architecture called RetU-Net that segments abnormal retinopathy lesion structures by retaining higher-level feature is proposed. The experiments are conducted using Indian Diabetic Retinopathy Image Dataset provided in “Diabetic Retinopathy: Segmentation and Grading Challenge” initially. The results have been compared with other state-of-art CNN architectures. Further evaluation is carried out on public datasets: STARE and DRIVE, and the performance is compared with U-Net based architectures used for retinal image segmentation. The proposed approach is efficient in terms of precision, sensitivity, specificity and accuracy. It received accuracy scores of 0.9612, 0.9712 while experimenting with STARE and DRIVE datasets respectively.