Many major diseases can cause changes in the morphology of blood vessels, and the segmentation of retinal blood vessels is of great significance for preventing these diseases. Obtaining complete, continuous, and high-resolution segmentation results is very challenging due to the diverse structures of retinal tissues, the complex spatial structures of blood vessels, and the presence of many small ships. In recent years, deep learning networks like UNet have been widely used in medical image processing. However, the continuous down-sampling operations in UNet can result in the loss of a significant amount of information. Although skip connections between the encoder and decoder can help address this issue, the encoder features still contain a large amount of irrelevant information that cannot be efficiently utilized by the decoder. To alleviate the irrelevant information, this paper proposes a feature selection module between the decoder and encoder that utilizes the self-attention mechanism of transformers to accurately and efficiently select the relevant encoder features for the decoder. Additionally, a lightweight Residual Global Context module is proposed to obtain dense global contextual information and establish dependencies between pixels, which can effectively preserve vascular details and segment small vessels accurately and continuously. Experimental results on three publicly available color fundus image datasets (DRIVE, CHASE, and STARE) demonstrate that the proposed algorithm outperforms existing methods in terms of both performance metrics and visual quality.

Improving the performance of flocking control policies in practical scenarios is of great value in promoting the practical application of multiagent flocking collaborative control algorithms. In this article, concerning the practicality of flocking algorithms in stochastic communication environments, we propose a model learning based multiagent flocking control algorithm. First, an agent motion model construction method based on sequential attention mechanisms is proposed to provide a more realistic agent motion model for environmental interaction. Considering the cooperation and equivalence of agents in the flocking task, a multiagent cooperative soft actor–critic (MACSAC) algorithm is proposed to optimize the control policy model. Then, a digital learning system for multiagent flocking collaborative control is constructed by combining the learned motion model with the MACSAC algorithm. Finally, we design a behavior reasoning (BR) model based on the prior control policy, and introduce the model into the MACSAC algorithm to infer the motion state of noncommunicating adjacent agents, which solves the problem of poor control policy caused by the information loss of observation state in stochastic communication environments. The experimental results indicate that the constructed digital learning system can effectively simulate the policy learning of multiagent flocking in actual environmental scenarios, and demonstrate that the designed BR model can effectively improve the performance of the MACSAC-based multiagent flocking collaborative control algorithm in stochastic communication environments.