Image-guided control of a robot for medical ultrasound

Abolmaesumi, Purang; Salcudean, Septimiu E.; Zhu, Wen-Hong; Sirouspour, M.R.; DiMaio, Simon P.

doi:10.1109/70.988970

Cited by 259 publications

(157 citation statements)

References 32 publications

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“…Robotic systems in medical contexts are seldom controlled over distance, but by an expert physically sitting next to the robot (e.g., [6], [7], and [8]). This obviously does not solve the problem of low geographical availability of doctors.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Other fields of applications for tele-operated medical robots are minimal invasive surgery [12], examining carotid arteries [7], identifying breast cancer [13], monitoring arteries for cardiovascular diseases [6], or taking ultrasound images of patients' arms [14]. A variety of medical robots have been implemented for ultrasound examination, especially for examinations of the abdomen (e.g., [15], [16], and [17]).…”

Section: Background and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

User requirements for a medical robotic system: Enabling doctors to remotely conduct ultrasonography and physical examination

Stollnberger

Moser

Giuliani

et al. 2016

2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

User requirements for a medical robotic system: Enabling doctors to remotely conduct ultrasonography and physical examination

Stollnberger

Moser

Giuliani

et al. 2016

2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

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“…Segmenting foreground objects from unconstrained video sequences is an important problem in computer vision and at the core of many visual computing technologies, such as event detection [1], 3D reconstruction [2], target tracking [3], and robotic visual servo manipulation [4] of unknown objects. A successful segmentation algorithm should be robust under a wide variety of dynamic scenes encountered in complex natural environments.…”

Section: Introductionmentioning

confidence: 99%

Large Displacement Dynamic Scene Segmentation through Multiscale Saliency Flow

Zhang

2016

IEICE Trans. Inf. & Syst.

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SUMMARY Most unsupervised video segmentation algorithms are difficult to handle object extraction in dynamic real-world scenes with large displacements, as foreground hypothesis is often initialized with no explicit mutual constraint on top-down spatio-temporal coherency despite that it may be imposed to the segmentation objective. To handle such situations, we propose a multiscale saliency flow (MSF) model that jointly learns both foreground and background features of multiscale salient evidences, hence allowing temporally coherent top-down information in one frame to be propagated throughout the remaining frames. In particular, the top-down evidences are detected by combining saliency signature within a certain range of higher scales of approximation coefficients in wavelet domain. Saliency flow is then estimated by Gaussian kernel correlation of non-maximal suppressed multiscale evidences, which are characterized by HOG descriptors in a high-dimensional feature space. We build the proposed MSF model in accordance with the primary object hypothesis that jointly integrates temporal consistent constraints of saliency map estimated at multiple scales into the objective. We demonstrate the effectiveness of the proposed multiscale saliency flow for segmenting dynamic real-world scenes with large displacements caused by uniform sampling of video sequences.

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“…In [1], the three in-plane motions of an US probe are controlled to automatically center in the US image the cross-section of an artery while an out-of-plane translation of the probe is manually performed. Five features extraction methods based on image similarity measures and contour segmentations are compared to track the artery center.…”

Section: Introductionmentioning

confidence: 99%

Automatic Tracking of an Organ Section with an Ultrasound Probe: Compensation of Respiratory Motion

Nadeau

Krupa

Gangloff

2011

Lecture Notes in Computer Science

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Abstract. In minimally invasive surgery or needle insertion procedures, the ultrasound imaging can easily and safely be used to visualize the target to reach. However the manual stabilization of the view of this target, which undergoes the physiological motions of the patient, can be a challenge for the surgeon. In this paper, we propose to perform this stabilization with a robotic arm equipped with a 2D ultrasound probe. The six degrees of freedom of the probe are controlled by an image-based approach, where we choose as visual feedback the image intensity. The accuracy of the control law is ensured by the consideration of the periodicity of the physiological motions in a predictive controller. Tracking tasks performed on a realistic abdominal phantom validate the proposed approach and its robustness to deformation is assessed on a gelatin-made deformable phantom.

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Image-guided control of a robot for medical ultrasound

Cited by 259 publications

References 32 publications

User requirements for a medical robotic system: Enabling doctors to remotely conduct ultrasonography and physical examination

User requirements for a medical robotic system: Enabling doctors to remotely conduct ultrasonography and physical examination

Large Displacement Dynamic Scene Segmentation through Multiscale Saliency Flow

Automatic Tracking of an Organ Section with an Ultrasound Probe: Compensation of Respiratory Motion

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