RESUMEN
Peripapillary atrophy (PPA) is a clinical abnormality related to many eye diseases, such as myopia and glaucoma. The shape and area of PPA are essential indicators of disease progression. PPA segmentation is a challenging task due to blurry edge and limited labeled data. In this paper, we propose a novel semi-supervised PPA segmentation method enhanced by prior knowledge. In order to learn shape information in the network, a novel shape constraint module is proposed to restrict the PPA appearance based on active shape model. To further leverage large amount of unlabeled data, a Siamese-like model updated by exponential moving average is introduced to provide pseudo labels. The pseudo labels are further refined by region connectivity correction. Extensive experiments on a clinical dataset demonstrate that our proposed PPA segmentation method provides good qualitative and quantitative performance.
RESUMEN
Objective.A semi-supervised learning method is an essential tool for applying medical image segmentation. However, the existing semi-supervised learning methods rely heavily on the limited labeled data. The generalization performance of image segmentation is improved to reduce the need for the number of labeled samples and the difficulty of parameter tuning by extending the consistency regularization.Approach.We propose a new regularization-driven Mean Teacher model based on semi-supervised learning for medical image segmentation in this work. We introduce a regularization-driven strategy with virtual adversarial training to improve segmentation performance and the robustness of the Mean Teacher model. We optimize the unsupervised loss function and the regularization term with an entropy minimum to smooth the decision boundary.Main results.We extensively evaluate the proposed method on the International Skin Imaging Cooperation 2017(ISIC2017) and COVID-19 CT segmentation datasets. Our proposed approach gains more accurate results on challenging 2D images for semi-supervised medical image segmentation. Compared with the state-of-the-art methods, the proposed approach has significantly improved and is superior to other semi-supervised segmentation methods.Significance.The proposed approach can be extended to other medical segmentation tasks and can reduce the burden of physicians to some extent.