Generative Adversarial Learning for Semi-supervised Retinal Layer Segmentation in OCT Images

It is often challenging to obtain large number of labeled data for retinal layer segmentation in optical coherence tomography scans due to the need for expert ophthalmologists. On the other hand, huge quantities of unlabeled scans are often collected in medical centers. In this work, we propose a novel generative adversarial learning framework, GOctSeg, for semi-supervised retinal layer segmentation. GOctSeg consists of a U-Net generator, a discriminator, and a segmentor. The generation of synthetic images from synthetic labels is performed with the U - Net generator, which is used as input together with labeled B-scan patches into the Segmentor network for retinal layer boundary regression. We have also evaluated our methodology on two datasets, the Data Resource for Multiple Sclerosis and Healthy Controls and the Duke University Diabetic Macular Edema dataset and demonstrated that our method outperformed or is comparable to other state-of-the-art methods with limited labels for boundary regression. Furthermore, we investigated the performance of GOctSeg on images with low signal-to-noise ratio or with blurred boundaries and showed that our methodology remained robust. Through ablation studies, we demonstrated the utility of synthetic labels for generative learning to guide in semi-supervised retinal layer segmentation. We envision that this methodology can be used to significantly reduce the effort required to obtain labels when there is label scarcity in clinical settings.