Localization and Control of Magnetic Suture Needles in Cluttered Surgical Site with Blood and Tissue

TL;DR

This paper presents a neural network-based localization and control system for magnetic surgical needles, achieving sub-3mm accuracy in challenging environments with blood and tissue, enabling autonomous suturing.

Contribution

It introduces a combined neural network and classical technique for needle localization and demonstrates autonomous control in complex surgical environments.

Findings

Localization RMS error of 0.73 mm in clean environments

Localization RMS error of 2.72 mm in blood and tissue environments

Needle following accuracy with tip position error between 2.6 mm and 3.7 mm

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 non-linear 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.