Transfer Learning with U-Net type model for Automatic Segmentation of Three Retinal Layers In Optical Coherence Tomography Images

Abstract

Retinal layer analysis on OCT images is a standard procedure used by ophthalmologists to diagnose various diseases. Due to a large number of generated OCT images for each patient, a manual image analysis can be time-consuming and error-prone, which can consequently affect the timeliness and quality of the diagnosis. Therefore, in recent years, a variety of methods, based prevalently on deep learning, have been proposed for the automatic segmentation of retinal layers. In our study, the U-Net type model with a ResNet based encoder, pretrained on ImageNet dataset is utilized. In addition, the model is combined with postprocessing step to obtain segmented layer boundaries. The modified versions of U-Net type model have already been applied to various non-medical imaging segmentation tasks, achieving outstanding results. To investigate whether the pretrained U-Net type model contributes to improvement of retinal layer segmentation, two models are trained and validated on 23 volumes of OCT images with age related macular degeneration (AMD): the U-Net model with pretrained ResNet34 encoder on ImageNet dataset and the original U-Net model, trained from the scratch. The one-sided Wilcoxon signed-rank test has shown that the pretrained U-Net type model outperforms the original U-Net model for segmenting three regions bounded by four layer boundaries.