Autonomous Scanning for Endomicroscopic Mosaicing and 3D Fusion

TL;DR

This paper presents an autonomous system using the da Vinci robot for precise endomicroscopic scanning, mosaicing, and 3D fusion, enabling enhanced visualization of tissue during minimally invasive surgery.

Contribution

It introduces a visual servoing approach for high-precision autonomous probe scanning, real-time mosaicing, and 3D fusion in robotic-assisted surgery.

Findings

Achieves sub-millimetre accuracy in probe positioning.

Generates large-area mosaics with minimal surgeon intervention.

Provides real-time 3D fused visualization of tissue.

Abstract

Robotic-assisted Minimally Invasive Surgery (RMIS) can benefit from the automation of common, repetitive or well-defined but ergonomically difficult tasks. One such task is the scanning of a pick-up endomicroscopy probe over a complex, undulating tissue surface in order to enhance the effective field-of-view through video mosaicing. In this paper, the da Vinci surgical robot, through the dVRK framework, is used for autonomous scanning and 2D mosaicing over a user-defined region of interest. To achieve the level of precision required for high quality large-area mosaic generation, which relies on sufficient overlap between consecutive image frames, visual servoing is performed using a tracking marker attached to the probe. The resulting sub-millimetre accuracy of the probe motion allows for the generation of large endomicroscopy mo- saics with minimal intervention from the surgeon. It also allows the probe to be maintained in an orientation perpendicular to the local tissue surface, providing optimal imaging results. Images are streamed from the endomicroscope and overlaid live onto the surgeons view, while 2D mosaics are generated in real-time, and fused into a 3D stereo reconstruction of the surgical scene, thus providing intuitive visualisation and fusion of the multi-scale images. The system therefore offers significant potential to enhance surgical procedures, by providing the operator with cellular-scale information over a larger area than could typically be achieved by manual scanning.