Automated Extraction of Surgical Needles from Tissue Phantoms

Sundaresan, Priya; Thananjeyan, Brijen; Chiu, Johnathan; Fer, Danyal; Goldberg, Ken

doi:10.1109/coase.2019.8843089

Cited by 24 publications

(15 citation statements)

References 31 publications

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“…Robotic surgery involves high stakes and complex environments. While gradual progress is being made to automate parts of surgery [3], [4], they require tailor-made 'classical' solutions to individual sub-tasks. Reinforcement learning(RL), on the other hand, offers the potential benefit of being applicable to multiple environmental conditions and tasks without requiring a human to generate specific solutions once conditions change.…”

Section: Introductionmentioning

confidence: 99%

Robotic Surgery With Lean Reinforcement Learning

Barnoy,

O'Brien,

Wang

et al. 2021

Preprint

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As surgical robots become more common, automating away some of the burden of complex direct human operation becomes ever more feasible. Model-free reinforcement learning (RL) is a promising direction toward generalizable automated surgical performance, but progress has been slowed by the lack of efficient and realistic learning environments. In this paper, we describe adding reinforcement learning support to the da Vinci Skill Simulator, a training simulation used around the world to allow surgeons to learn and rehearse technical skills. We successfully teach an RL-based agent to perform sub-tasks in the simulator environment, using either image or state data. As far as we know, this is the first time an RL-based agent is taught from visual data in a surgical robotics environment. Additionally, we tackle the sample inefficiency of RL using a simple-to-implement system which we term hybrid-batch learning (HBL), effectively adding a second, long-term replay buffer to the Q-learning process. Additionally, this allows us to bootstrap learning from images from the data collected using the easier task of learning from state. We show that HBL decreases our learning times significantly.

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Section: Introductionmentioning

confidence: 99%

Robotic Surgery With Lean Reinforcement Learning

Barnoy,

O'Brien,

Wang

et al. 2021

Preprint

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“…Six common surgical tasks appear to be widely investigated for automation. For the suturing task, including the works related to knot tying and needle insertion, we reported the following: [16], [70], [85], [88], [89], [161], [178], [195]- [197], [203], [243], and [246]. The pick, transfer, and place task was mainly characterized by experiments relying on pegs and rings from the fundamentals of the laparoscopic surgery training paradigm [54], [69], [91], [96], [102], [151]- [153], [163], [264] or new surgical tools [177].…”

Section: Instrument Controlmentioning

confidence: 99%

Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit

D'Ettorre

Mariani

Stilli

et al. 2021

IEEE Robot. Automat. Mag.

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This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.R obotic-assisted surgery is now well established in clinical practice and has become the gold-standard clinical treatment option for several clinical indications. The field of robotic-assisted surgery is expected to grow substantially in the next decade, with a range of new robotic devices emerging to address unmet clinical needs across different specialties. A vibrant surgical robotics research community is pivotal for conceptualizing such new systems as well as for developing and training the engineers and scientists to translate them into practice. The da Vinci Research Kit (dVRK), an academic and industry collaborative effort to repurpose decommissioned da Vinci surgical systems [Intuitive Surgical Inc. (ISI), California, USA] as a research platform for surgical robotics research, has been a key initiative for addressing a barrier to entry for new research groups in surgical robotics. In this article, we present an extensive review of the publications that have been facilitated by the dVRK over the past decade. We classify research efforts into different categories and outline some of the major challenges and needs for the robotics community to maintain and build upon this initiative.

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“…Suturing requires a wide range of considerations for effective automation including needle manipulation [6], knot tying [7], identification of entry and exit points for the suture throw [8], and interfacing the automation with the surgeon for effective deployment [9]. In this work, we investigate the specific sub-challenge of needle manipulation from the 1 Zih-Yun Chiu, Florian Richter, and Michael C. Yip are with the Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093 USA.…”

Section: Introductionmentioning

confidence: 99%

Bimanual Regrasping for Suture Needles using Reinforcement Learning for Rapid Motion Planning

Chiu

Richter

Funk

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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Regrasping a suture needle is an important process in suturing, and previous study has shown that it takes on average 7.4s before the needle is thrown again. To bring efficiency into suturing, prior work either designs a taskspecific mechanism or guides the gripper toward some specific pick-up point for proper grasping of a needle. Yet, these methods are usually not deployable when the working space is changed. These prior efforts highlight the need for more efficient regrasping and more generalizability of a proposed method. Therefore, in this work, we present rapid trajectory generation for bimanual needle regrasping via reinforcement learning (RL). Demonstrations from a sampling-based motion planning algorithm is incorporated to speed up the learning. In addition, we propose the ego-centric state and action spaces for this bimanual planning problem, where the reference frames are on the end-effectors instead of some fixed frame. Thus, the learned policy can be directly applied to any robot configuration and even to different robot arms. Our experiments in simulation show that the success rate of a single pass is 97%, and the planning time is 0.0212s on average, which outperforms other widely used motion planning algorithms. For the real-world experiments, the success rate is 73.3% if the needle pose is reconstructed from an RGB image, with a planning time of 0.0846s and a run time of 5.1454s. If the needle pose is known beforehand, the success rate becomes 90.5%, with a planning time of 0.0807s and a run time of 2.8801s.

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Automated Extraction of Surgical Needles from Tissue Phantoms

Cited by 24 publications

References 31 publications

Robotic Surgery With Lean Reinforcement Learning

Robotic Surgery With Lean Reinforcement Learning

Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit

Bimanual Regrasping for Suture Needles using Reinforcement Learning for Rapid Motion Planning

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