An Efficient Weakly-Supervised Learning Method for Optic Disc Segmentation

Accurate optic disc segmentation plays an essential role in the early diagnosis of glaucoma, which has been a major cause of irreversible blindness for the past decade. Recently, U-shape Convolutional Neural Network (CNN) models have achieved favourable performance in optic disc segmentation. However, it is worth noting that these models require a large number of pixel-level annotations while these annotations are difficult to obtain in clinical practice. As a solution, weakly-supervised training methods are commonly implemented, but it will provoke U-shape CNN generating inaccurate, diluted, and grid-like segmentation results. In this paper, we propose a novel Hybrid Network (HyNet) to solve the issue above. HyNet consists of a U-shape backbone hybridized with a cross-scale connection structure, which makes better use of multi-scale visual semantics. Nevertheless, the generalization ability of HyNet is affected by the domain shift among different datasets. Therefore, we innovatively combine weakly- and fully-supervised training methods, namely Hybrid Process (HyProcess), to solve the domain shift problem. Experimental results on ONHSD, DRIONS-DB, and DRISHTI-GS datasets show that our model outperforms the state-of-the-art, reaching Dice of 82.39(%), 93.72(%), and 95.34(%) respectively. Additionally, our ablation study validates the effectiveness of HyNet along with HyProcess, and further analysis reflects their value in clinical practice.