Partially-Supervised Learning for Vessel Segmentation in Ocular Images

Abstract

The vessel segmentation in ocular images is a fundamental and important step in the diagnosis of eye-related diseases. Existing vessel segmentation methods require a large-scale ocular images with pixel-level annotations. However, manually annotating masks is a laborious and tedious process. Compared with the traditional pipelines which either annotate the complete training set or several images in full, in this paper, we propose a novel supervision manner, named Partially-Supervised Learning (PSL), which only relies on partial annotations in the form of one patch from each of the few images. Targeting it, we propose an active learning framework with latent MixUp. The active learning strategy is employed to select the most informative patch for further annotation, while the latent MixUp is proposed to learn a proper visual representation of both the annotated and unannotated patches. The experimental results on two types of vessel segmentation datasets (Rose-1 (SVC) dataset for OCTA image, and DRIVE dataset for fundus image) validate the effectiveness of our model. With only 5% annotations on Rose-1 (SVC) and DRIVE dataset, our performance is comparable with the previous methods trained on the whole fully annotated dataset.