Towards real-time multiple surgical tool tracking

Real-time surgical tool tracking and pose estimation using a hybrid cylindrical marker

International Journal of Computer Assisted Radiology and Surgery ◽

10.1007/s11548-017-1558-9 ◽

2017 ◽

Vol 12 (6) ◽

pp. 921-930 ◽

Cited By ~ 16

Author(s):

Lin Zhang ◽

Menglong Ye ◽

Po-Ling Chan ◽

Guang-Zhong Yang

Keyword(s):

Real Time ◽

Pose Estimation ◽

Surgical Tool ◽

Tool Tracking

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Weakly supervised segmentation for real‐time surgical tool tracking

Healthcare Technology Letters ◽

10.1049/htl.2019.0083 ◽

2019 ◽

Vol 6 (6) ◽

pp. 231-236 ◽

Cited By ~ 1

Author(s):

Eung‐Joo Lee ◽

William Plishker ◽

Xinyang Liu ◽

Shuvra S. Bhattacharyya ◽

Raj Shekhar

Keyword(s):

Real Time ◽

Surgical Tool ◽

Supervised Segmentation ◽

Weakly Supervised ◽

Tool Tracking

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Automatic Initialization and Failure Detection for Surgical Tool Tracking in Retinal Microsurgery

Informatik aktuell - Bildverarbeitung für die Medizin 2017 ◽

10.1007/978-3-662-54345-0_78 ◽

2017 ◽

pp. 346-351

Author(s):

Josué Page Vizcaíno ◽

Nicola Rieke ◽

David Joseph Tan ◽

Federico Tombari ◽

Abouzar Eslami ◽

...

Keyword(s):

Failure Detection ◽

Automatic Initialization ◽

Surgical Tool ◽

Tool Tracking

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An Anchor-Free Convolutional Neural Network for Real-Time Surgical Tool Detection in Robot-Assisted Surgery

IEEE Access ◽

10.1109/access.2020.2989807 ◽

2020 ◽

Vol 8 ◽

pp. 78193-78201 ◽

Cited By ~ 2

Author(s):

Yuying Liu ◽

Zijian Zhao ◽

Faliang Chang ◽

Sanyuan Hu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Real Time ◽

Robot Assisted ◽

Surgical Tool ◽

Robot Assisted Surgery

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Surgical tool tracking by on-line selection of structural correlation filters

2017 25th European Signal Processing Conference (EUSIPCO) ◽

10.23919/eusipco.2017.8081627 ◽

2017 ◽

Author(s):

Daniel Wesierski ◽

Anna Jezierska

Keyword(s):

Correlation Filters ◽

Structural Correlation ◽

Surgical Tool ◽

On Line ◽

Line Selection ◽

Selection Of ◽

Tool Tracking

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Integration of Vision and Inertial Sensors for a Surgical Tool Tracking

Dynamic Systems and Control, Parts A and B ◽

10.1115/imece2006-15763 ◽

2006 ◽

Author(s):

N. Parnian ◽

M. F. Golnaraghi

Keyword(s):

Inertial Sensors ◽

Vision System ◽

Estimation Algorithm ◽

Optical Data ◽

Mems Devices ◽

Motion Modeling ◽

Surgical Tool ◽

Short Period ◽

Tool Motion ◽

Tool Tracking

This paper represents a hybrid Vision/INS system in a microsurgical tool tracking application. Surgical MEMS devices must not only cope with all of the challenges that conventional MEMS devices have, but also address the integration of electronics and signal processing, calibration, reliability, accuracy and testing. A hybrid Vision/INS system with the integration of the Extended Kalman Filter precisely calculates 6D position-orientation of a microsurgical tool during surgery. This configuration guarantees the real-time tracking of the instrument. Ultimately, the vision system supports the IMU to deal with the drift problem but the position error increases dramatically in the absence of the vision system. In this paper, the tool motion modeling is proposed to bind the error in the acceptable range for a short period of missing data. The motion of the tool is modeled and updated at any time that the instrument is in the camera view field. This model is applied to the estimation algorithm whenever the camera is not in line of site and the optical data is missing.

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Real Time Techniques for Nonlinear Tissue Deformation in Surgical Simulation

ASME 2007 Summer Bioengineering Conference ◽

10.1115/sbc2007-172518 ◽

2007 ◽

Author(s):

Suvranu De ◽

Yi-Je Lim

Keyword(s):

Finite Element ◽

Real Time ◽

Surgical Simulation ◽

Computational Technique ◽

Finite Element Models ◽

Geometrically Nonlinear ◽

Time Performance ◽

Surgical Tool ◽

Surgical Simulations ◽

Tissue Deformations

The requirement of real time performance, crucial to multimodal surgical simulations, imposes severe demands in terms of computational efficiency. A physics-based meshfree computational technique known as the Point-Associated Finite Field (PAFF) approach has been developed to circumvent many outstanding problems associated with traditional mesh-based computational schemes and has been applied in this paper to the modeling of geometrically nonlinear tissue deformations. The technique is based on a novel combination of multiresolution approach coupled with a fast reanalysis scheme in which the response predicted by an underlying linear PAFF model is enhanced in the local neighborhood of the surgical tool-tip by a nonlinear model. We present performance comparisons of PAFF with traditional finite element models.

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