Beveled-Tip Needle-Steering Using 3D Ultrasound, Mechanical-Based Kalman Filter and Curvilinear ROI Prediction

Paul Mignon 1, 2 Philippe Poignet 1 Jocelyne Troccaz 2
1 DEXTER - Conception et commande de robots pour la manipulation
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 TIMC-IMAG-GMCAO - Gestes Medico-chirurgicaux Assistés par Ordinateur
TIMC-IMAG - Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble - UMR 5525
Abstract : This paper introduces a new robust 3D ultrasound needle detection approach integrated in a 3D needle steering system associated to a real-time path planning. The robustness of an existing algorithm is improved by limiting the needle detection to a curvilinear region of interest (ROI) using a novel mechanical-based prediction model. This linear model is also used in a Kalman filter to reduce detection noise and reject false detections. These two improvements drastically increase quality of our feedback. Finally, the 3D needle steering system is able to reach a target in phantoms with a maximal error of 0.8 mm without obstacle and 1.6 mm with obstacle.
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Paul Mignon, Philippe Poignet, Jocelyne Troccaz. Beveled-Tip Needle-Steering Using 3D Ultrasound, Mechanical-Based Kalman Filter and Curvilinear ROI Prediction. ICCARV: International Conference on Control, Automation, Robotics and Vision, Nov 2016, Phuket, Thailand. pp.1-6, ⟨10.1109/ICARCV.2016.7838840⟩. ⟨hal-01455325⟩

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