Tumor localization using automated palpation with Gaussian Process Adaptive Sampling

Garg, Animesh; Sen, Siddarth; Kapadia, Rishi; Jen, Yiming; McKinley, Stephen; Miller, Lauren; Goldberg, Ken

doi:10.1109/coase.2016.7743380

Cited by 62 publications

(43 citation statements)

References 22 publications

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“…Such a set can then be used for robust specification of the alloy composition. Similar examples are abound in other domains [7]. Hence, level set estimation is an important problem.…”

Section: Introductionmentioning

confidence: 82%

Level Set Estimation with Search Space Warping

Senadeera

Rana

Gupta

et al. 2020

Advances in Knowledge Discovery and Data Mining

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This paper proposes a new method of level set estimation through search space warping using Bayesian optimisation. Instead of a single solution, a level set offers a range of solutions each meeting the goal and thus provides useful knowledge in tolerance for industrial product design. The proposed warping scheme increases performance of existing level set estimation algorithmsin particular the ambiguity acquisition function. This is done by constructing a complex covariance function to warp the Gaussian Process. The covariance function is designed to expand regions deemed to have a high potential for being at the desired level whilst contracting others. Subsequently, Bayesian optimisation using this covariance function ensures that the level set is sampled more thoroughly. Experimental results demonstrate increased efficiency of level set discovery using the warping scheme. Theoretical analysis concerning warping the covariance function, maximum information gain and bounds on the cumulative regret are provided.

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“…Such a set can then be used for robust specification of the alloy composition. Similar examples are abound in other domains [7]. Hence, level set estimation is an important problem.…”

Section: Introductionmentioning

confidence: 82%

Level Set Estimation with Search Space Warping

Senadeera

Rana

Gupta

et al. 2020

Advances in Knowledge Discovery and Data Mining

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“…Over the last two years, Bayesian optimization-based methods have gained popularity [2], [4], [5], [12]. These methods model the stiffness map using a Gaussian process regression (GPR) and reduce the exploration time by directing the robot to stiff regions.…”

Section: B Tumor Search Approachesmentioning

confidence: 99%

“…This work has been funded through the National Robotics Initiative by NSF grant IIS-1426655. While the works in literature deal with force sensing [10], [11], tumor localization [2], [4]- [6], [12] and graphical image overlays [13]- [16], there is a gap in literature when it comes to systems that deal with all these issues at the same time. For example, Yamamoto et al [16] deal with tumor localization and visual overlay, but they assume the organ is flat and place the organ on a force sensing plate, which is not representative of a surgical scenario.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Yamamoto et al [16] deal with tumor localization and visual overlay, but they assume the organ is flat and place the organ on a force sensing plate, which is not representative of a surgical scenario. On the other hand, Garg et al [2] use a palpation probe mounted on a da Vinci research kit (dVRK) tool [17]. However, they do not deal with registering the organ or visual overlay of the estimated stiffness map.…”

Section: Introductionmentioning

confidence: 99%

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A surgical system for automatic registration, stiffness mapping and dynamic image overlay

Zevallos

Srivatsan

Salman

et al. 2018

2018 International Symposium on Medical Robotics (ISMR)

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In this paper we develop a surgical system using the da Vinci research kit (dVRK) that is capable of autonomously searching for tumors and dynamically displaying the tumor location using augmented reality. Such a system has the potential to quickly reveal the location and shape of tumors and visually overlay that information to reduce the cognitive overload of the surgeon. We believe that our approach is one of the first to incorporate state-of-the-art methods in registration, force sensing and tumor localization into a unified surgical system. First, the preoperative model is registered to the intra-operative scene using a Bingham distribution-based filtering approach. An active level set estimation is then used to find the location and the shape of the tumors. We use a recently developed miniature force sensor to perform the palpation. The estimated stiffness map is then dynamically overlaid onto the registered preoperative model of the organ. We demonstrate the efficacy of our system by performing experiments on phantom prostate models with embedded stiff inclusions.

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“…Existing surgical robotics platforms rely on pure teleoperation through a master-slave interface where the surgeon fully controls the motions of the robot. To reduce tedium and fatigue in long or repetitive procedures, recent work has highlighted several opportunities for autonomous execution of surgical subtasks [36] including debridement [15], suturing [24,[31][32][33], and palpation for tumor detection [6].…”

Section: Introductionmentioning

confidence: 99%

Fast and Reliable Autonomous Surgical Debridement with Cable-Driven Robots Using a Two-Phase Calibration Procedure

Seita¹,

Krishnan²,

Fox³

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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Automating precision subtasks such as debridement (removing dead or diseased tissue fragments) with Robotic Surgical Assistants (RSAs) such as the da Vinci Research Kit (dVRK) is challenging due to inherent non-linearities in cabledriven systems. We propose and evaluate a novel two-phase coarse-to-fine calibration method. In Phase I (coarse), we place a red calibration marker on the end effector and let it randomly move through a set of open-loop trajectories to obtain a large sample set of camera pixels and internal robot end-effector configurations. This coarse data is then used to train a Deep Neural Network (DNN) to learn the coarse transformation bias. In Phase II (fine), the bias from Phase I is applied to move the end-effector toward a small set of specific target points on a printed sheet. For each target, a human operator manually adjusts the end-effector position by direct contact (not through teleoperation) and the residual compensation bias is recorded. This fine data is then used to train a Random Forest (RF) to learn the fine transformation bias. Subsequent experiments suggest that without calibration, position errors average 4.55mm. Phase I can reduce average error to 2.14mm and the combination of Phase I and Phase II can reduces average error to 1.08mm. We apply these results to debridement of raisins and pumpkin seeds as fragment phantoms. Using an endoscopic stereo camera with standard edge detection, experiments with 120 trials achieved average success rates of 94.5%, exceeding prior results with much larger fragments (89.4%) and achieving a speedup of 2.1x, decreasing time per fragment from 15.8 seconds to 7.3 seconds. Source code, data, and videos are available at https://sites.google.com/ view/calib-icra/.

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Tumor localization using automated palpation with Gaussian Process Adaptive Sampling

Cited by 62 publications

References 22 publications

Level Set Estimation with Search Space Warping

Level Set Estimation with Search Space Warping

A surgical system for automatic registration, stiffness mapping and dynamic image overlay

Fast and Reliable Autonomous Surgical Debridement with Cable-Driven Robots Using a Two-Phase Calibration Procedure

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