Strain estimation of moving tissue based on automatic motion compensation by ultrasound visual servoing

Patlan-Rosales, Pedro A.; Krupa, Alexandre

doi:10.1109/iros.2017.8206128

Cited by 5 publications

(1 citation statement)

References 20 publications

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“…However, most of the current industrial robots are not able to control the deformation of an object because it requires both knowledge of the object physical properties and real-time tracking of the deformations. Several studies have focused on real-time deformation tracking [4]- [7] but not simultaneously with deformation control. A few methods have been proposed to find a grasp configuration on a deformable object [8] but without interactive control of the deformation.…”

Section: Introductionmentioning

confidence: 99%

Active Deformation through Visual Servoing of Soft Objects

Lagneau

Krupa

Marchal

2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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In this paper, we propose the ADVISEd (Active Deformation through VIsual SErvoing) method, a novel modelfree deformation servoing method able to deform a soft object towards a desired shape. ADVISEd relies on an online estimation of the deformation Jacobian that relates the motion of the robot end-effector to the deformation behavior of the object. The estimation is based on a weighted least-squares minimization with a sliding window. The robustness of the method to observation noise is ensured using an eigenvaluebased confidence criterion. The ADVISEd method is validated through comparisons with a model-based and a model-free state-of-the-art methods. Two experimental setups are proposed to compare the methods, one to perform a marker-based active shaping task and one to perform several marker-less active shaping and shape preservation tasks. Experiments showed that our approach can interactively control the deformations of an object in different tasks while ensuring better robustness to external perturbations than the state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Active Deformation through Visual Servoing of Soft Objects

Lagneau

Krupa

Marchal

2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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Enabling quantitative robot-assisted compressional elastography via the extended Kalman filter

Napoli¹,

Goswami²,

McAleavey³

et al. 2021

Phys. Med. Biol.

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Compressional or quasi-static elastography has demonstrated the capability to detect occult cancers in a variety of tissue types, however it has a serious limitation in that the resulting elastograms are generally qualitative whereas other forms of elastography, such as shear-wave, can produce absolute measures of elasticity for histopathological classification. We address this limitation by introducing a stochastic method using an extended Kalman filter and robot-assistance to obtain quantitative elastograms which are resilient to measurement noise and system uncertainty. In this paper, the probabilistic framework is described, which utilizes many ultrasound acquisitions obtained from multiple palpations, to fuse data and uncertainty from a robotic manipulator’s joint encoders and force/torque sensor directly into the inverse reconstruction of the elastogram. Quantitative results are demonstrated over homogeneous and inclusion gelatin phantoms using a seven degree of freedom manipulator for a range of initial elasticity assumptions. Results imply resilience to poorly assumed initial conditions as all trials were within 5 kPa of the elasticity measured by a mechanical testing system. Moreover, the presence or absence of an inclusion is clear in all reconstructed elastograms even when artifacts are present in displacement fields, indicating further robustness to measurement noise. The proposed stochastic method allows fusion of data from a robot’s sensors directly into compressional elastography image reconstruction which may stabilize optimization and improve accuracy. This approach provides a mathematical framework to readily incorporate measurements from additional sensors in future applications which may extend the capabilities of compressional elastography beyond that of producing quantitative elasticity measurements.

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Toward intraoperative endomicroscopy with a GPU-accelerated deformable video mosaicking algorithm

Gong

Zuo

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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Strain estimation of moving tissue based on automatic motion compensation by ultrasound visual servoing

Cited by 5 publications

References 20 publications

Active Deformation through Visual Servoing of Soft Objects

Active Deformation through Visual Servoing of Soft Objects

Enabling quantitative robot-assisted compressional elastography via the extended Kalman filter

Toward intraoperative endomicroscopy with a GPU-accelerated deformable video mosaicking algorithm

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