Localization and Control of Magnetic Suture Needles in Cluttered Surgical Site with Blood and Tissue
Will Pryor, Yotam Barnoy, Suraj Raval, Xiaolong Liu, Lamar Mair, Daniel Lerner, Onder Erin, Gregory D. Hager
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 0.73mm RMS error in clean environments and 2.72mm 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 2.6mm to 3.7mm RMS, opening the door towards autonomous suturing tasks.
BibTeX
@inproceedings{iros2021_localizationandc,
title = {Localization and Control of Magnetic Suture Needles in Cluttered Surgical Site with Blood and Tissue},
author = {Will Pryor and Yotam Barnoy and Suraj Raval and Xiaolong Liu and Lamar Mair and Daniel Lerner and Onder Erin and Gregory D. Hager and Yancy Diaz-Mercado and Axel Krieger},
booktitle = {IROS 2021},
year = {2021}
}