Retinal vessel segmentation plays a crucial role in medical image analysis, aiding ophthalmologists in disease diagnosis, monitoring, and treatment guidance. However, due to the complex boundary structure and rich texture features in retinal blood vessel images, existing methods have challenges in the accurate segmentation of blood vessel boundaries. In this study, we propose the texture-driven Swin-UNet with enhanced boundary-wise perception. Firstly, we designed a Cross-level Texture Complementary Module (CTCM) to fuse feature maps at different scales during the encoding stage, thereby recovering detailed features lost in the downsampling process. Additionally, we introduced a Pixel-wise Texture Swin Block (PT Swin Block) to improve the model’s ability to localize vessel boundary and contour information. Finally, we introduced an improved Hausdorff distance loss function to further enhance the accuracy of vessel boundary segmentation. The proposed method was evaluated on the DRIVE and CHASEDB1 datasets, and the experimental results demonstrate that our model obtained superior performance in terms of Accuracy (ACC), Sensitivity (SE), Specificity (SP), and F1 score (F1), and the accuracy of vessel boundary segmentation was significantly improved.

Retinal vein cannulation (RVC) is a surgical procedure utilized in the treatment of retinal vein occlusion. During RVC, the micropuncture process encounters challenges due to unstable contact between the bent needle and the retinal vein, potentially resulting in micropuncture failure. To enhance the success rate, a robotic micropuncture procedure with preload strategy is proposed, which can increase the tissue stiffness and improve stability. First, a nonlinear viscoelastic (NV) model is developed to characterize the nonlinearity and relaxation behavior of the preload force. Subsequently, an NV model based adaptive integral terminal sliding mode controller (NAITSMC) is designed to ensure precise force tracking under tissue uncertainty and respiratory motion. The effectiveness of the robotic micropuncture is validated through in vitro experiments conducted on open-sky porcine eyes. The experimental results indicate that the NV model is more suitable for modeling the force applied to the retinal vein under various inner pressure cases. Under simulated respiratory motion, the NAITSMC demonstrates statistically reduction in error compared to the advanced sliding mode force controller. The preload strategy significantly improves the success rate, achieving a maximum rate of 90%. Furthermore, a preload force of 40 μN is recommended, demonstrating a higher success rate while maintaining acceptable safety considerations.

In biological micromanipulation, cell penetration is a typical procedure that precedes cell injection or oocyte enucleation. During this procedure, cells usually undergo significant deformation, which leads to cell damage. In this paper, we focus on modeling and evaluating the cell penetration process to reduce cell deformation and stress, thereby reducing cell damage. Initially, a finite element model (FEM) is established to simulate the cell penetration process. The effectiveness of the model is then verified through visual detection and comparison of cell deformation with experimental data. Next, various mechanical responses are analyzed, considering the influence of parameters, such as the radius and shape of the injection micropipettes, material properties, and size of the cells. Finally, the relationship between the intracellular stress and the cell penetration depth of biological cells is obtained. The evaluation results will be applied to develop optimized operation plans, enhancing the efficiency and safety of the cell penetration process.