The automatic segmentation of retinal vessels is of great significance for the analysis and diagnosis of retinal related diseases. However, the imbalanced data in retinal vascular images remain a great challenge. Current image segmentation methods based on deep learning almost always focus on local information in a single image while ignoring the global information of the entire dataset. To solve the problem of data imbalance in optical coherence tomography angiography (OCTA) datasets, this paper proposes a medical image segmentation method (contrastive OCTA segmentation net, COSNet) based on global contrastive learning. First, the feature extraction module extracts the features of OCTA image input and maps them to the segment head and the multilayer perceptron (MLP) head, respectively. Second, a contrastive learning module saves the pixel queue and pixel embedding of each category in the feature map into the memory bank, generates sample pairs through a mixed sampling strategy to construct a new contrastive loss function, and forces the network to learn local information and global information simultaneously. Finally, the segmented image is fine tuned to restore positional information of deep vessels. The experimental results show the proposed method can improve the accuracy (ACC), the area under the curve (AUC), and other evaluation indexes of image segmentation compared with the existing methods. This method could accomplish segmentation tasks in imbalanced data and extend to other segmentation tasks.

. Due to significant performance in the representation of data points, non-negative matrix factorization (NMF) has been widely applied in machine-learning fields, such as dimension reduction, image representation, feature extraction, data mining, and so on. However, classical NMF suffers from a common issue, low efficiency in representing the internal geometric structure of data and sparsity limitation. To circumvent this problem, we innovatively propose a semi-supervised NMF algorithm called semi-supervised dual-graph regularization non-negative matrix factorization (LOSDNMF), into which dual-graph and bi-orthogonal constraints are embedded to reduce the inconsistency between the original matrix and the basic vectors while maintaining the manifold structures of the data and feature spaces. This strategy can fully explore the potential geometry information of the data, which is extremely beneficial to enhance the learning ability of the model. In addition, the local coordinate constraints are introduced to ensure good sparsity of the coefficient matrix and simplify the calculation. Furthermore, an iterative updating scheme for the optimization problem of LOSDNMF and its convergence proofs are also provided in detail. The effectiveness of the proposed method is verified on eight benchmark datasets. Experimental results show that our method can effectively improve clustering performance.