Real-time biopsy needle tip estimation in 2D ultrasound images

Mathiassen, Kim; Dall’Alba, Diego; Muradore, Riccardo; Fiorini, Paolo; Elle, Ole Jakob

doi:10.1109/icra.2013.6631195

Cited by 18 publications

(15 citation statements)

References 15 publications

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“…A 3D optical tracking system (Accutrack 500, Atracsys LLC, a system with active markers and a mean position error of 0.19 mm) is used to estimate relative coordinate transformations among the robots and the phantom. Finally, the setup includes an ultrasound imaging device whose images are visualized on a dedicated graphical interface for the surgeons and processed in real time to detect the position of needle tip, using the algorithm developed by Mathiassen et al [32], and provide intraoperative adaptation of robot motion trajectories, as required by the surgical workflow previously described. For the suturing task, the ISUR robot is equipped with two arms (i.e.…”

Section: Robotic Setupmentioning

confidence: 99%

“…3.2, specifying the event-driven behavior required to coordinate robot actions and supervise the overall task execution. The cognitive part of the system is completed by Sensing software, which is in this case implementing real-time US image processing for needle tracking [32], and a Situation Awareness module, implementing Bayesian Networks [44] processing data received from Sensing and robot control software to detect events and exceptions (e.g. forbidden regions touched, force limits exceeded, etc.…”

Section: Implementation and Deploymentmentioning

confidence: 99%

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A Cognitive Robot Control Architecture for Autonomous Execution of Surgical Tasks

Preda

Ferraguti

Rossi

et al. 2016

J. Med. Robot. Res.

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The research on medical robotics is starting to address the autonomous execution of surgical tasks, without effective intervention of humans apart from supervision and task configuration. This paper addresses the complete automation of a surgical robot by combining advanced sensing, cognition and control capabilities, developed according to rigorous assessment of surgical require- ments, formal specification of robotic system behavior and software design and implementation based on solid tools and frame- works. In particular, the paper focuses on the cognitive control architecture and its development process, based on formal modeling and verification methods as best practices to ensure safe and reliable behavior. Full implementation of the proposed architecture has been tested on an experimental setup including a novel robot specifically designed for surgical applications, but adaptable to different selected tasks (i.e. needle insertion, wound suturing)

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Section: Robotic Setupmentioning

confidence: 99%

Section: Implementation and Deploymentmentioning

confidence: 99%

A Cognitive Robot Control Architecture for Autonomous Execution of Surgical Tasks

Preda

Ferraguti

Rossi

et al. 2016

J. Med. Robot. Res.

Self Cite

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“…An 3-D optical tracking system is used to estimate relative coordinate transformations among the robots and the phantom. Finally, the setup includes an ultrasound imaging device whose images can be visualized on a dedicated graphical interface for the surgeons and processed in real-time to detect the position of needle tip, as shown in Mathiassen et al (2013), and provide intra-operative adaptation of robot motion trajectories.…”

Section: Case Study and Robotic Setupmentioning

confidence: 99%

Distributed Control Architecture for Automated Surgical Task Execution with Coordinated Robot Arms

Bonfè

Preda

Secchi

et al. 2014

IFAC Proceedings Volumes

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“…In addition, Ayvaci et al [18] performed biopsy needle segmentation on TRUS videos for use in MRI/TRUS fusion guided biopsy. Work on needle tip tracking has been performed by Mathiassen et al [19], who developed an optical tracking system based on intensity features in the images and Neubach et al [20], who used a 30…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Needle Shape Estimation in TRUS-Guided Prostate Brachytherapy Using 2-D Ultrasound Images

Waine

Rossa

Sloboda

et al. 2016

IEEE J. Biomed. Health Inform.

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Abstract-In this paper we propose an automated method to reconstruct the 3D needle shape during needle insertion procedures using only 2D transverse ultrasound (US) images. Using a set of transverse US images, image processing and random sample consensus (RANSAC) is used to locate the needle within each image and estimate the needle shape. The method is validated with an in-vitro needle insertion setup and a transparent tissue phantom, where two orthogonal cameras are used to capture the true 3D needle shape for verification. Results showed that the use of at least 3 images obtained at 75% of the maximum insertion depth or greater allows for maximum needle shape estimation errors of less than 2 mm. In addition, the needle shape can be calculated consistently as long as the needle can be identified in 30% of the transverse US images obtained. Application to permanent prostate brachytherapy (PPB) is also presented, where the estimated needle shape is compared to manual segmentation and sagittal US images. Our method is intended to help assess needle placement during manual or robotassisted needle insertion procedures after the needle has been inserted.

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Real-time biopsy needle tip estimation in 2D ultrasound images

Cited by 18 publications

References 15 publications

A Cognitive Robot Control Architecture for Autonomous Execution of Surgical Tasks

A Cognitive Robot Control Architecture for Autonomous Execution of Surgical Tasks

Distributed Control Architecture for Automated Surgical Task Execution with Coordinated Robot Arms

Three-Dimensional Needle Shape Estimation in TRUS-Guided Prostate Brachytherapy Using 2-D Ultrasound Images

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