Image‐Based 3D Pose Reconstruction of Surgical Needle for Robot‐Assisted Laparoscopic Suturing

Zhong, Fangxun; Liu, Yunhui

doi:10.1049/cje.2017.06.009

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…To address this challenge, motion features are explored in [124], while a Kalman filter is proposed in [125]. Optimization techniques based on gradient descent algorithms are adopted in [126] along with geometric needle models for tracking. Although the results of [124] reports a localization accuracy of 1.70mm while respecting the real-time constraint [121], there are no contributions addressing the problem of high level feature tracking in a realistic scenario, thus motivating further investigation in the field.…”

Section: High Level Feature Trackingmentioning

confidence: 99%

Autonomy in Surgical Robotics

Attanasio

Scaglioni

Momi

et al. 2021

Annu. Rev. Control Robot. Auton. Syst.

140

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This review examines the dichotomy between automatic and autonomous behaviors in surgical robots, maps the possible levels of autonomy of these robots, and describes the primary enabling technologies that are driving research in this field. It is organized in five main sections that cover increasing levels of autonomy. At level 0, where the bulk of commercial platforms are, the robot has no decision autonomy. At level 1, the robot can provide cognitive and physical assistance to the surgeon, while at level 2, it can autonomously perform a surgical task. Level 3 comes with conditional autonomy, enabling the robot to plan a task and update planning during execution. Finally, robots at level 4 can plan and execute a sequence of surgical tasks autonomously. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 4 is May 3, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Section: High Level Feature Trackingmentioning

confidence: 99%

Autonomy in Surgical Robotics

Attanasio

Scaglioni

Momi

et al. 2021

Annu. Rev. Control Robot. Auton. Syst.

140

View full text Add to dashboard Cite

show abstract

“…We have proposed an efficient and autonomous robot-camera calibration approach to compute the robotic instrument’s pose from a monocular camera for vision-guided instrument manipulation (Zhong et al, 2020). Aiming for autonomous suturing, a monocular-based 6-degree-of-freedom (6-DoF) pose estimation algorithm of a surgical needle has been developed (Zhong et al, 2016; Zhong & Liu, 2018), which was later implemented to a dual-arm needle insertion control scheme to increase needle insertion accuracy under deformation (Zhong et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Integrated planning and control of robotic surgical instruments for task autonomy

Zhong

Liu

2023

The International Journal of Robotics Research

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Agile maneuvers are essential for robot-enabled complex tasks such as surgical procedures. Prior explorations on surgery autonomy are limited to feasibility study of completing a single task without systematically addressing generic manipulation safety across different tasks. We present an integrated planning and control framework for 6-DoF robotic instruments for pipeline automation of surgical tasks. We leverage the geometry of a robotic instrument and propose the nodal state space to represent the robot state in SE (3) space. Each elementary robot motion could be encoded by regulation of the state parameters via a dynamical system. This theoretically ensures that every in-process trajectory is globally feasible and stably reached to an admissible target, and the controller is of closed-form without computing 6-DoF inverse kinematics. Then, to plan the motion steps reliably, we propose an interactive (instant) goal state of the robot that transforms manipulation planning through desired path constraints into a goal-varying manipulation (GVM) problem. We detail how GVM could adaptively and smoothly plan the procedure (could proceed or rewind the process as needed) based on on-the-fly situations under dynamic or disturbed environment. Finally, we extend the above policy to characterize complete pipelines of various surgical tasks. Simulations show that our framework could smoothly solve twisted maneuvers while avoiding collisions. Physical experiments using the da Vinci Research Kit validates the capability of automating individual tasks including tissue debridement, dissection, and wound suturing. The results confirm good task-level consistency and reliability compared to state-of-the-art automation algorithms.

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“…Recently, deep neural networks have demonstrated superior performance to that of other machine learning techniques in computer vision tasks [ 38 ] , such as image classification [ 39 ] and semantic segmentation [ 40 ] . These deep networks have recently been used in areas such as bioinformatics [ 41 ] and medical imaging [ 42 , 43 ] . In particular, U‐net's excellent performance in the field of medical image segmentation has revitalized research in the field of hippocampal segmentation and has greatly improved the segmentation precision and speed compared with the atlas‐based method [ 44 , 45 ] .…”

Section: Introductionmentioning

confidence: 99%

Hippocampal Segmentation in Brain MRI Images Using Machine Learning Methods: A Survey

Pan

Liu

et al. 2021

Chinese J of Electronics

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The hippocampus is closely related to many brain diseases, such as Alzheimer's disease. Accurate measurement of the hippocampus is helpful for clinicians in identifying lesions and then diagnosing and treating the related brain diseases. Therefore, accurate segmentation of the hippocampus is of vital significance for the in‐depth study of many brain diseases. However, the accurate measurement of the hippocampus depends on its accurate segmentation, and hippocampal segmentation has always been a challenging problem due to the small size, irregular shape, and fuzzy boundaries with surrounding tissues of the hippocampus. With the development of machine learning, many innovative methods have been proposed to segment the hippocampus. The purpose of this survey is to provide a comprehensive overview of hippocampal segmentation in brain MRI images using machine learning methods. First, a brief introduction to hippocampal segmentation in brain MRI images is given. Then, common evaluation metrics of hippocampal segmentation are introduced. Next, brain hippocampal segmentation methods based on traditional machine learning and deep learning are described. Subsequently, some common open datasets and toolkits applied to brain hippocampal segmentation are presented. Finally, objective conclusions regarding hippocampal segmentation in brain MRI images using machine learning methods are drawn, and future developments and trends are identified for brain hippocampal segmentation.

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Image‐Based 3D Pose Reconstruction of Surgical Needle for Robot‐Assisted Laparoscopic Suturing

Cited by 6 publications

References 11 publications

Autonomy in Surgical Robotics

Autonomy in Surgical Robotics

Integrated planning and control of robotic surgical instruments for task autonomy

Hippocampal Segmentation in Brain MRI Images Using Machine Learning Methods: A Survey

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