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Zengshuo Wang

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3 papers

1 author row

IROS Conference 2025 Conference Paper

Automated Dual-Micropipette Coordination Microinjection for Batch Zebrafish Larvae Based on Pose Estimation

Can Wang
Rongxin Liu
Huiying Gong
Zengshuo Wang
Lu Zhou
Yaowei Liu
Xin Zhao 0010
Mingzhu Sun

Zebrafish are widely used in the biomedical field, as an ideal model for microinjection. In automated zebrafish microinjection, posture adjustment is the first and key step, which takes a lot of skill, and injection success assessment is a challenging task. Constrained by these two aspects, it is difficult to further enhance the efficiency and success rate of injection. In this study, we propose an automated dual-micropipette coordination microinjection system. Zebrafish are randomly arranged in our system, reducing the operational difficulty, and the yolk is positioned using a pose estimation algorithm, followed by injection accomplished with dual-micropipette. Due to the reduction of posture adjustment time by half, the proposed system achieves the shortest injection time of 15. 2s. Moreover, the simplicity of the system and the ease of operation contribute to the clinical feasibility of our system.

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IROS Conference 2025 Conference Paper

Nonlinear Viscoelastic Model-based Deformation Optimization for Robotic Micropuncture in Retinal Vein Cannulation

Bo Hu 0013
Ruimin Li
Shiyu Xu
Rongxin Liu
Zengshuo Wang
Mingzhu Sun
Xin Zhao 0010

Micropuncture is a critical step in drug injection during retinal vein cannulation (RVC) surgery. Minimizing deformation during the micropuncture process is beneficial to reduce mechanical damage. However, this goal is challenging due to the viscoelastic characteristics of retinal tissue. In this paper, a robotic micropuncture scheme for deformation optimization that incorporates a nonlinear force model is proposed. Before micropuncture, a preload strategy is utilized to ensure stable contact between needle and retinal vein. Secondly, a nonlinear viscoelastic (NV) model is developed to characterize the nonlinearity and relaxation behavior of the tissue. Finally, a speed optimization framework, based on the NV model and physical constraint, is adopted to minimize deformation. The effectiveness of the proposed scheme is validated through in vitro experiments conducted on open-sky porcine eyes. With average force error of 1. 48 μN, stable contact can be achieved via proportion-integral-differential controller. The experimental results demonstrate that the NV model is more suitable for force modeling of retinal tissue. Furthermore, the optimized speed results in an average deformation of 0. 5727 mm, which represents a reduction of at least 21. 02% compared to the linear model. Thanks to the proposed scheme, the robotic micropuncture based on a varying speed trajectory can reduce deformation and enhance the safety of RVC surgery.

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IROS Conference 2022 Conference Paper

Simultaneous Depth Estimation and Localization for Cell Manipulation Based on Deep Learning

Zengshuo Wang
Huiying Gong
Ke Li 0026
Bin Yang
Yue Du
Yaowei Liu
Xin Zhao 0010
Mingzhu Sun

Visual localization, which is a key technology to realize the automation of cell manipulation, has been widely studied. Since the depth of field of the microscope is narrow, the planar localization and depth estimation are usually coupled together. At present, most methods adopt the serial working mode of focusing first and then planar localization, but they usually do not have good real-time performance and stability. In this paper, a simultaneous depth estimation and localization network was developed for cell manipulation. The network takes a focused image and a defocus-offset image as inputs, and outputs the defocus in the depth direction and the offset in the plane at the same time after going through defocus-offset information extraction, defocus classification mapping and offset regression mapping. To train and test our network, we also create two datasets: An Adherent Cell dataset and an Injection Micropipette dataset. The experimental results demonstrated that the proposed method achieves the detection of all test samples with a frame rate of more than 40Hz, and the maximum errors of depth estimation and localization are $\boldsymbol{2. 44\mu m}$ and $\boldsymbol{0. 49\mu m}$, respectively. The proposed method has good stability, which is mainly reflected in its strong generalization ability and anti-noise ability.

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