Autonomous robotic palpation: Machine learning techniques to identify hard inclusions in soft tissues

Nichols, Kirk A.; Okamura, Allison M.

doi:10.1109/icra.2013.6631198

Cited by 35 publications

(14 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A list of relevant subtasks in the research of surgical automation is compiled in Table 1. [19] peg transfer stereo camera training phantom medium low [20] The automation of a number of subtasks are currently under active research, such as different aspects of suturing, soft tissue cutting, debridement, palpation or blunt dissection, employing techniques like learning-by-observation, motion decomposition and state machines [7][8][9][10][11][12][13][14][15][16][17][18][19][20]. All of the mentioned surgical subtasks are to be performed on soft tissue, in a highly deformable environment.…”

Section: Subtask Automation In Surgerymentioning

confidence: 99%

A DVRK-based Framework for Surgical Subtask Automation

2019

APH

View full text Add to dashboard Cite

Robotic assistance is becoming a standard in Minimally Invasive Surgery. Despite its clinical benefits and technical potential, surgeons still have to perform manually a number of monotonous and time-consuming surgical subtasks, like knot-tying or blunt dissection. Many believe that the next bold step in the advancement of robotic surgery is the automation of such subtasks. Partial automation can reduce the cognitive load on surgeons, and support them in paying more attention to the critical elements of the surgical workflow. Our aim was to develop a software framework to ease and hasten the automation of surgical subtasks. This framework was built alongside the Da Vinci Research Kit (DVRK), while it can be ported onto other robotic platforms, since it is based on the Robot Operating System (ROS). The software includes both stereo vision-based and hierarchical motion planning, with a wide palette of often used surgical gestures-such as grasping, cutting or soft tissue manipulation-as building blocks to support the high-level implementation of autonomous surgical subtask execution routines. This open-source surgical automation framework-named irob-saf-is available at https://github.com/ABC-iRobotics/irob-saf.

show abstract

Section: Subtask Automation In Surgerymentioning

confidence: 99%

A DVRK-based Framework for Surgical Subtask Automation

2019

APH

View full text Add to dashboard Cite

show abstract

“…Servoing based on optical and ultrasound imaging has been used for autonomous control [10,11]. Other researchers performed surgical tasks including suturing [5,6,10], cutting and debriding [7,12], and palpating [13].…”

Section: Introductionmentioning

confidence: 99%

A paced shared-control teleoperated architecture for supervised automation of multilateral surgical tasks

Shamaei

Che

Murali

et al. 2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Self Cite

View full text Add to dashboard Cite

Automation of repetitive tasks can improve laparoscopic surgical procedures by unloading surgeons and reducing duration, trauma, and expense. However, surgical procedures involve delicate manipulation of deformable tissues in a very dynamic environment, suggesting that automated execution of surgical tasks should be carried out under the supervision of the surgeon. We propose a teleoperated architecture that allows a surgeon to employ and supervise agents that can autonomously perform or assist with surgical tasks. The architecture is independent of the automation method. It includes a dominance factor that allows the surgeon to take control over the slave robot at any time, and an aggressiveness factor that sets the performance pace of the autonomous agent. We tested the architecture during execution of a multilateral tension-and-cut task, where a human operator and an autonomous agent are responsible for tensioning or cutting of a tissue. The architecture allowed for supervised and paced automation of the task. We found that collaboration of the human operator and autonomous agent can lead to shorter completion time compared to performance of only a human.

show abstract

“…In view of the advantage of autonomous robotic palpation, [22] analyzed the force modulation strategies for robotic palpation to identify the hard region in soft tissue through a learning from demonstration (LfD) based method. Moreover, in [23] presented a remote palpation using a machine learning approach and research in [24] illustrated an autonomous surface recognition using vibration signals.…”

Section: Introductionmentioning

confidence: 99%

Compensating Uncertainties in Force Sensing for Robotic-Assisted Palpation

2019

View full text Add to dashboard Cite

Force sensing in robotic-assisted minimally invasive surgery (RMIS) is crucial for performing dedicated surgical procedures, such as bilateral teleoperation and palpation. Due to the bio-compatibility and sterilization requirements, a specially designed surgical tool/shaft is normally attached to the sensor while contacting the organ targets. Through this design, the measured force from the sensor usually contains uncertainties, such as noise, inertial force etc., and thus cannot reflect the actual interaction force with the tissue environment. Motivated to provide the authentic contact force between a robotic tool and soft tissue, we proposed a data-driven force compensation scheme without intricate modeling to reduce the effects of force measurement uncertainties. In this paper, a neural-network-based approach is utilized to automatically model the inertial force subject to noise during the robotic palpation procedure, then the exact contact force can be obtained through the force compensation method which cancels the noise and inertial force. Following this approach, the genuine interaction force during the palpation task can be achieved furthermore to improve the appraisal of the tumor surrounded by the soft tissue. Experiments are conducted with robotic-assisted palpation tasks on a silicone-based soft tissue phantom and the results verify the effectiveness of the suggested method.

show abstract

Autonomous robotic palpation: Machine learning techniques to identify hard inclusions in soft tissues

Cited by 35 publications

References 14 publications

A DVRK-based Framework for Surgical Subtask Automation

A DVRK-based Framework for Surgical Subtask Automation

A paced shared-control teleoperated architecture for supervised automation of multilateral surgical tasks

Compensating Uncertainties in Force Sensing for Robotic-Assisted Palpation

Contact Info

Product

Resources

About