Autonomous Tissue Scanning under Free-Form Motion for Intraoperative Tissue Characterisation

In Minimally Invasive Surgery (MIS), tissue scanning with imaging probes is required for subsurface visualisation to characterise the state of the tissue. However, scanning of large tissue surfaces in the presence of deformation is a challenging task for the surgeon. Recently, robot-assisted local tissue scanning has been investigated for motion stabilisation of imaging probes to facilitate the capturing of good quality images and reduce the surgeon's cognitive load. Nonetheless, these approaches require the tissue surface to be static or deform with periodic motion. To eliminate these assumptions, we propose a visual servoing framework for autonomous tissue scanning, able to deal with free-form tissue deformation. The 3D structure of the surgical scene is recovered and a feature-based method is proposed to estimate the motion of the tissue in real-time. A desired scanning trajectory is manually defined on a reference frame and continuously updated using projective geometry to follow the tissue motion and control the movement of the robotic arm. The advantage of the proposed method is that it does not require the learning of the tissue motion prior to scanning and can deal with free-form deformation. We deployed this framework on the da Vinci surgical robot using the da Vinci Research Kit (dVRK) for Ultrasound tissue scanning. Since the framework does not rely on information from the Ultrasound data, it can be easily extended to other probe-based imaging modalities.Comment: 7 pages, 5 figures, ICRA 202

Automated pick-up of suturing needles for robotic surgical assistance

Robot-assisted laparoscopic prostatectomy (RALP) is a treatment for prostate cancer that involves complete or nerve sparing removal prostate tissue that contains cancer. After removal the bladder neck is successively sutured directly with the urethra. The procedure is called urethrovesical anastomosis and is one of the most dexterity demanding tasks during RALP. Two suturing instruments and a pair of needles are used in combination to perform a running stitch during urethrovesical anastomosis. While robotic instruments provide enhanced dexterity to perform the anastomosis, it is still highly challenging and difficult to learn. In this paper, we presents a vision-guided needle grasping method for automatically grasping the needle that has been inserted into the patient prior to anastomosis. We aim to automatically grasp the suturing needle in a position that avoids hand-offs and immediately enables the start of suturing. The full grasping process can be broken down into: a needle detection algorithm; an approach phase where the surgical tool moves closer to the needle based on visual feedback; and a grasping phase through path planning based on observed surgical practice. Our experimental results show examples of successful autonomous grasping that has the potential to simplify and decrease the operational time in RALP by assisting a small component of urethrovesical anastomosis

Robot Autonomy for Surgery

Autonomous surgery involves having surgical tasks performed by a robot operating under its own will, with partial or no human involvement. There are several important advantages of automation in surgery, which include increasing precision of care due to sub-millimeter robot control, real-time utilization of biosignals for interventional care, improvements to surgical efficiency and execution, and computer-aided guidance under various medical imaging and sensing modalities. While these methods may displace some tasks of surgical teams and individual surgeons, they also present new capabilities in interventions that are too difficult or go beyond the skills of a human. In this chapter, we provide an overview of robot autonomy in commercial use and in research, and present some of the challenges faced in developing autonomous surgical robots

Epipolar geometry for vision-guided laser surgery.

International audienceThe μRALP project involves the development of a system for endoluminal laser phonosurgery, i.e. surgery of the vocal chords using a laser emitted from inside the larynx. Indeed, in current laryngeal laser surgical procedures, a beam of incision laser is projected on the target position of the soft tissue from the working distance of 400mm by means of a rigid laryngoscope (Fig.1). This yields safety concerns for the patient and staff, as well as limitations to accuracy. More, this so-called laryngeal suspension position of the patient requires an extreme extension of the neck, which makes it painful several days after the operation

Image moments-based ultrasound visual servoing

International audienceA new visual servoing method based on B-mode ultrasound images is proposed to automatically control the motion of a 2D ultrasound probe held by a medical robot in order to reach a desired B-scan image of an object of interest. In this approach, combinations of image moments extracted from the current observed object cross-section are used as feedback visual features. The analytical form of the interaction matrix, relating the time variation of these visual features to the probe velocity, is derived and used in the control law. Simulations performed with a static ultrasound volume containing an egg-shaped object, and in-vitro experiments using a robotized ultrasound probe that interacts with a rabbit heart immersed in water, show the validity of this new approach and its robustness with respect to modeling and measurements errors

Robust realtime instrument tracking in ultrasound images for visual servoing

Abstract-Minimally invasive surgery in combination with ultrasound (US) imaging imposes high demands on the surgeon's hand-eye-coordination capabilities. A possible solution to reduce these requirements is minimally invasive robotic surgery in which the instrument is guided by visual servoing towards the goal defined by the surgeon in the US image. This approach requires robust tracking of the instrument in the US image sequences which is known to be difficult due to poor image quality. This paper presents computer vision algorithms and results of visual servoing experiments. Adaptive thresholding according to Otsu's method allows to cope with large intensity variations of the instrument echo. Subsequently applied morphological operations suppress noise and echo artefacts. A fast labelling algorithm based on run length coding allows for realtime labelling of the regions. A heuristic exploiting region size and region velocity helps to overcome ambiguities. The overall computation time is less than 10 ms per frame on a standard PC. The tracking algorithm requires no information about texture and shape which are known to be very unreliable in US image sequences. Experimental results for different instrument materials (polyvinyl chloride, polyurethane, nylon, and plexiglas) are given, illustrating the performance of the proposed approach: when chosing the appropriate material the reconstructed trajectories are smooth and only few outliers occur. As a consequence, the visual servoing loop showed to be robust and stable

Medical image computing and computer-aided medical interventions applied to soft tissues. Work in progress in urology

Until recently, Computer-Aided Medical Interventions (CAMI) and Medical Robotics have focused on rigid and non deformable anatomical structures. Nowadays, special attention is paid to soft tissues, raising complex issues due to their mobility and deformation. Mini-invasive digestive surgery was probably one of the first fields where soft tissues were handled through the development of simulators, tracking of anatomical structures and specific assistance robots. However, other clinical domains, for instance urology, are concerned. Indeed, laparoscopic surgery, new tumour destruction techniques (e.g. HIFU, radiofrequency, or cryoablation), increasingly early detection of cancer, and use of interventional and diagnostic imaging modalities, recently opened new challenges to the urologist and scientists involved in CAMI. This resulted in the last five years in a very significant increase of research and developments of computer-aided urology systems. In this paper, we propose a description of the main problems related to computer-aided diagnostic and therapy of soft tissues and give a survey of the different types of assistance offered to the urologist: robotization, image fusion, surgical navigation. Both research projects and operational industrial systems are discussed

Robotic Ultrasound Imaging: State-of-the-Art and Future Perspectives

Ultrasound (US) is one of the most widely used modalities for clinical intervention and diagnosis due to the merits of providing non-invasive, radiation-free, and real-time images. However, free-hand US examinations are highly operator-dependent. Robotic US System (RUSS) aims at overcoming this shortcoming by offering reproducibility, while also aiming at improving dexterity, and intelligent anatomy and disease-aware imaging. In addition to enhancing diagnostic outcomes, RUSS also holds the potential to provide medical interventions for populations suffering from the shortage of experienced sonographers. In this paper, we categorize RUSS as teleoperated or autonomous. Regarding teleoperated RUSS, we summarize their technical developments, and clinical evaluations, respectively. This survey then focuses on the review of recent work on autonomous robotic US imaging. We demonstrate that machine learning and artificial intelligence present the key techniques, which enable intelligent patient and process-specific, motion and deformation-aware robotic image acquisition. We also show that the research on artificial intelligence for autonomous RUSS has directed the research community toward understanding and modeling expert sonographers' semantic reasoning and action. Here, we call this process, the recovery of the "language of sonography". This side result of research on autonomous robotic US acquisitions could be considered as valuable and essential as the progress made in the robotic US examination itself. This article will provide both engineers and clinicians with a comprehensive understanding of RUSS by surveying underlying techniques.Comment: Accepted by Medical Image Analysi