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Localization and Control of Magnetic Suture Needles in Cluttered Surgical Site with Blood and Tissue.
Pryor, Will; Barnoy, Yotam; Raval, Suraj; Liu, Xiaolong; Mair, Lamar; Lerner, Daniel; Erin, Onder; Hager, Gregory D; Diaz-Mercado, Yancy; Krieger, Axel.
Afiliação
  • Pryor W; Department of Computer Science, Johns Hopkins University, Baltimore, MD 21211 USA.
  • Barnoy Y; Department of Computer Science, Johns Hopkins University, Baltimore, MD 21211 USA.
  • Raval S; Department of Mechanical Engineering, University of Maryland, College Park, MD 20742 USA.
  • Liu X; Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21211 USA.
  • Mair L; Weinberg Medical Physics, Inc., North Bethesda, MD 20852 USA.
  • Lerner D; Department of Mechanical Engineering, University of Maryland, College Park, MD 20742 USA.
  • Erin O; Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21211 USA.
  • Hager GD; Department of Computer Science, Johns Hopkins University, Baltimore, MD 21211 USA.
  • Diaz-Mercado Y; Department of Mechanical Engineering, University of Maryland, College Park, MD 20742 USA.
  • Krieger A; Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21211 USA.
Rep U S ; 2021: 524-531, 2021.
Article em En | MEDLINE | ID: mdl-35223133
ABSTRACT
Real-time visual localization of needles is necessary for various surgical applications, including surgical automation and visual feedback. In this study we investigate localization and autonomous robotic control of needles in the context of our magneto-suturing system. Our system holds the potential for surgical manipulation with the benefit of minimal invasiveness and reduced patient side effects. However, the nonlinear magnetic fields produce unintuitive forces and demand delicate position-based control that exceeds the capabilities of direct human manipulation. This makes automatic needle localization a necessity. Our localization method combines neural network-based segmentation and classical techniques, and we are able to consistently locate our needle with 0.73 mm RMS error in clean environments and 2.72 mm RMS error in challenging environments with blood and occlusion. The average localization RMS error is 2.16 mm for all environments we used in the experiments. We combine this localization method with our closed-loop feedback control system to demonstrate the further applicability of localization to autonomous control. Our needle is able to follow a running suture path in (1) no blood, no tissue; (2) heavy blood, no tissue; (3) no blood, with tissue; and (4) heavy blood, with tissue environments. The tip position tracking error ranges from 2.6 mm to 3.7 mm RMS, opening the door towards autonomous suturing tasks.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: Rep U S Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: Rep U S Ano de publicação: 2021 Tipo de documento: Article