Methods to Segment Hard Inclusions in Soft Tissue During Autonomous Robotic Palpation

Nichols, Kirk A.; Okamura, Allison M.

doi:10.1109/tro.2015.2402531

Cited by 58 publications

(40 citation statements)

References 31 publications

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“…Many research groups have tried to achieve intraoperative tumor detection in minimally invasive surgery such as laparoscopic surgery. For instance, automated palpation systems have been developed with Gaussian process adaptive sampling, a force modulation strategy abstracted from human manual palpation, machine learning algorithms and simultaneous estimation of stiffness distribution and registration . Although automated palpation can achieve high accuracy and repeatability, the control scheme might be extremely difficult in a surgical situation.…”

Section: Introductionmentioning

confidence: 99%

Visual and tactile feedback for a direct‐manipulating tactile sensor in laparoscopic palpation

Fukuda

Tanaka

Kappers

et al. 2017

Robotics Computer Surgery

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Section: Introductionmentioning

confidence: 99%

Visual and tactile feedback for a direct‐manipulating tactile sensor in laparoscopic palpation

Fukuda

Tanaka

Kappers

et al. 2017

Robotics Computer Surgery

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“…In those in which the interaction of the robot with the environment is required, tactile perception is critical. In robotics surgery, tactile sensing plays an important role [ 4 , 5 , 6 ]. Moreover, in other applications related to medicine (e.g., for rehabilitation [ 7 , 8 ] or biomedical engineering [ 9 ]), tactile sensing is often used.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human–Robot Interaction

Gandarias

Gómez-de-Gabriel

García-Cerezo

2018

Sensors

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The use of tactile perception can help first response robotic teams in disaster scenarios, where visibility conditions are often reduced due to the presence of dust, mud, or smoke, distinguishing human limbs from other objects with similar shapes. Here, the integration of the tactile sensor in adaptive grippers is evaluated, measuring the performance of an object recognition task based on deep convolutional neural networks (DCNNs) using a flexible sensor mounted in adaptive grippers. A total of 15 classes with 50 tactile images each were trained, including human body parts and common environment objects, in semi-rigid and flexible adaptive grippers based on the fin ray effect. The classifier was compared against the rigid configuration and a support vector machine classifier (SVM). Finally, a two-level output network has been proposed to provide both object-type recognition and human/non-human classification. Sensors in adaptive grippers have a higher number of non-null tactels (up to 37% more), with a lower mean of pressure values (up to 72% less) than when using a rigid sensor, with a softer grip, which is needed in physical human–robot interaction (pHRI). A semi-rigid implementation with 95.13% object recognition rate was chosen, even though the human/non-human classification had better results (98.78%) with a rigid sensor.

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“…These works commonly direct the robot along a predefined path that scans the entire organ or region of interest [7], [8], [11], [12]. Some of these works [10], [13] use adaptive grid resolution to increase This work was was supported by NRI Large grants IIS-1426655 and IIS-1327566. 1 palpation resolution around boundaries of regions of interest marked by high stiffness gradients [10], [13].…”

Section: Introductionmentioning

confidence: 99%

“…Some of these works [10], [13] use adaptive grid resolution to increase This work was was supported by NRI Large grants IIS-1426655 and IIS-1327566. 1 palpation resolution around boundaries of regions of interest marked by high stiffness gradients [10], [13]. To detect the boundaries of stiff inclusions Nichols et al [13] used a support vector machine classifier to localize the boundaries of the tumors.…”

Section: Introductionmentioning

confidence: 99%

Trajectory-Optimized Sensing for Active Search of Tissue Abnormalities in Robotic Surgery

Salman

Ayvali

Srivatsan

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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In this work, we develop an approach for guiding robots to automatically localize and find the shapes of tumors and other stiff inclusions present in the anatomy. Our approach uses Gaussian processes to model the stiffness distribution and active learning to direct the palpation path of the robot. The palpation paths are chosen such that they maximize an acquisition function provided by an active learning algorithm. Our approach provides the flexibility to avoid obstacles in the robot's path, incorporate uncertainties in robot position and sensor measurements, include prior information about location of stiff inclusions while respecting the robot-kinematics. To the best of our knowledge this is the first work in literature that considers all the above conditions while localizing tumors. The proposed framework is evaluated via simulation and experimentation on three different robot platforms: 6-DoF industrial arm, da Vinci Research Kit (dVRK), and the Insertable Robotic Effector Platform (IREP). Results show that our approach can accurately estimate the locations and boundaries of the stiff inclusions while reducing exploration time.

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Methods to Segment Hard Inclusions in Soft Tissue During Autonomous Robotic Palpation

Cited by 58 publications

References 31 publications

Visual and tactile feedback for a direct‐manipulating tactile sensor in laparoscopic palpation

Visual and tactile feedback for a direct‐manipulating tactile sensor in laparoscopic palpation

Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human–Robot Interaction

Trajectory-Optimized Sensing for Active Search of Tissue Abnormalities in Robotic Surgery

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