Surgical subtask automation — Soft tissue retraction

Abstract

Robot-assisted surgery is becoming standard-of-care in minimally invasive surgery. Given the intense development in this area, many believe that the next big step is surgical subtask automation, the partial automation of certain elements of the procedure. Autonomous execution at lower task levels has the potential to safely improve one element of a surgical process. Automation by artificial intelligence may significantly improve surgery with better accuracy and targeting, that can shorten the recovering time of the patient. Furthermore, partial automation can also help surgeons efficiently by reducing the fatigue in the case of time-consuming operations. In this paper, we present the automation of soft tissue retraction, an often recurring subtask of surgical interventions. Soft tissue retraction plays an important role in laparoscopic cholecystectomy, e.g., during the exploration of the Calot triangle, automatic retraction would streamline the procedure. The presented method only relies on a stereo camera image feed, and therefore does not put additional overhead on the already crowded operating room. We developed and tested multiple control methods for soft tissue retraction built on each other: a simple proportional control for reference, one using Hidden Markov Models for state estimation, and one employing fuzzy logic. Our method was tested comparatively with all three controllers in a simplified phantom environment.

Autonomy in Surgical Robotics

“…The strategy aims at minimising the tissue strain, simultaneously avoiding tearing and guaranteeing an obstacle-free trajectory. Recent studies [88,89] present tissue retraction on a dVRK involving visual markers to identify the flap grasping point and fuzzy logic to execute the gesture. Despite the promising results, only bench-top experiments are available.…”

A Comparative Study of Spatio-Temporal U-Nets for Tissue Segmentation in Surgical Robotics

“…On the other hand, automation of tasks that involve tissue manipulation are challenging due to the complex geometry and compliance of the soft tissues. Few examples of autonomous tissue manipulation are available [16], [17], mostly demonstrated in simplified scenarios with reduced complexity. The main barrier for development of realistic applications is the complexity of the scene, difficult to analyse autonomously.…”

Autonomous Tissue Retraction in Robotic Assisted Minimally Invasive Surgery – A Feasibility Study

“…In [19] a simulation framework to perform a grasp-and-retract task is presented along with a path planning method for retraction in the presence of an obstacle is reported. More recently, advanced approaches have been proposed in [20], where retraction is controlled by an image-based system, and in [21], [22], where three different approaches based on proportional control, hidden Markov models and fuzzy logic are developed. In these works, the start and end points of the retraction are manually indicated by the surgeon thus entailing no autonomous planning.…”

“…This contribution advances the field of robotic-assisted MIS, laying the foundations for future developments in the field of autonomous surgical assistance. Compared to other works in soft tissue retraction, such as [20] and [21], we increase the level of autonomy by providing autonomous tissue segmentation and gesture planning abilities directly on the endoscopic video sequence. Our system is capable of automatically extracting start and end points for tissue retraction, thus reducing the input required from the surgeon in defining task specifications.…”