Robotic motion compensation for bone movement, using ultrasound images

Torres, Pedro; Gonçalves, Paulo J. S.; Martins, Jorge

doi:10.1108/ir-12-2014-0435

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…Therefore, an effective method to estimate the position of the hip centre in real time will need to be explored, using either conventional image processing or deep learning, so that the registration error caused by depth imaging noise can be bounded to a satisfactory range. Alternatively, ultrasound can be used intra-operatively to acquire part of the femur surface that is far away from the knee incision, as in [11], [12], such that the diaphysis direction can be better defined to improve the rotational accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Currently, optical tracking systems used by commercial orthopaedic robots can provide a minimum refresh rate of 20 Hz, which is the lowest requirement for smooth target tracking. Ultrasound can be used intraoperatively to provide images of the bone surface for registration [11], [12], though its speed and convenience for intra-operative use need further investigation. In this context, the development of depth imaging technology, typically adopted by different types of depth cameras, provides new possibilities for bone geometry measurement.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Markerless Registration and Tracking of the Bone for Computer-Assisted Orthopaedic Surgery

Baena

2020

IEEE Access

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To achieve a simple and less invasive registration procedure in computer-assisted orthopaedic surgery, we propose an automatic, markerless registration and tracking method based on depth imaging and deep learning. A depth camera is used to continuously capture RGB and depth images of the exposed bone during surgery, and deep neural networks are trained to first localise the surgical target using the RGB image, then segment the target area of the corresponding depth image, from which the surface geometry of the target bone can be extracted. The extracted surface is then compared to a pre-operative model of the same bone for registration. This process can be performed dynamically during the procedure at a rate of 5-6 Hz, without any need for surgeon intervention or invasive optical markers. Ex vivo registration experiments were performed on a cadaveric knee, and accuracy measurements against an optically tracked ground truth resulted in a mean translational error of 2.74 mm and a mean rotational error of 6.66 •. Our results are the first to describe a promising new way to achieve automatic markerless registration and tracking in computerassisted orthopaedic surgery, demonstrating that truly seamless registration and tracking of the limb is within reach. Our method reduces invasiveness by removing the need for percutaneous markers. The surgeon is also exempted from inserting markers and collecting registration points manually, which contributes to a more efficient surgical workflow and shorter procedure time in the operating room. INDEX TERMS Computer-assisted orthopaedic surgery, deep learning, depth imaging, markerless registration.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Markerless Registration and Tracking of the Bone for Computer-Assisted Orthopaedic Surgery

Baena

2020

IEEE Access

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“…2d ). A navigation assistant for markerless automatic motion compensation in a custom femur drilling LBR robot (KUKA) was developed by Torres et al [ 40 ] and evaluated on a bone phantom. The dynamic bone position and orientation were registered intrainterventionally with the image of a manually operated optically tracked ultrasound probe, and a preinterventional CT scan in which the target was defined.…”

Section: Collaborative Assistancementioning

confidence: 99%

Medical Robotics for Ultrasound Imaging: Current Systems and Future Trends

et al. 2021

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Purpose of Review This review provides an overview of the most recent robotic ultrasound systems that have contemporary emerged over the past five years, highlighting their status and future directions. The systems are categorized based on their level of robot autonomy (LORA). Recent Findings Teleoperating systems show the highest level of technical maturity. Collaborative assisting and autonomous systems are still in the research phase, with a focus on ultrasound image processing and force adaptation strategies. However, missing key factors are clinical studies and appropriate safety strategies. Future research will likely focus on artificial intelligence and virtual/augmented reality to improve image understanding and ergonomics. Summary A review on robotic ultrasound systems is presented in which first technical specifications are outlined. Hereafter, the literature of the past five years is subdivided into teleoperation, collaborative assistance, or autonomous systems based on LORA. Finally, future trends for robotic ultrasound systems are reviewed with a focus on artificial intelligence and virtual/augmented reality.

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“…In this work, the main purpose is to adapt the robot pose to possible unexpected motions while performing drilling tasks during a surgery. The robot pose adaptation is performed following the approach presented in (P. Torres, Gonçalves, and Martins 2015). The overall process is modeled with the OROSU ontology, which controls the robot's actions and sub-actions and allows the user to follow the sequence of those actions.…”

Section: Reasoning Scopementioning

confidence: 99%

A review and comparison of ontology-based approaches to robot autonomy

Olivares-Alarcos

Beßler

Khamis

et al. 2019

The Knowledge Engineering Review

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Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach is to represent these terms using ontology languages that conceptualize the respective domain. In this work, we will review projects that use ontologies to support robot autonomy. We will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain.

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Robotic motion compensation for bone movement, using ultrasound images

Cited by 7 publications

References 19 publications

Automatic Markerless Registration and Tracking of the Bone for Computer-Assisted Orthopaedic Surgery

Automatic Markerless Registration and Tracking of the Bone for Computer-Assisted Orthopaedic Surgery

Medical Robotics for Ultrasound Imaging: Current Systems and Future Trends

A review and comparison of ontology-based approaches to robot autonomy

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