Fast and robust semiautomatic liver segmentation with haptic interaction

Abstract. We present a method for semi-automatic segmentation of the liver from CT scans. True 3D interaction with haptic feedback is used to facilitate initialization, i.e., seeding of a fast marching algorithm. Four users initialized 52 datasets and the mean interaction time was 40 seconds. The segmentation accuracy was verified by a radiologist. Volume measurements and segmentation precision show that the method has a high reproducibility

Haptically assisted connection procedure for the reconstruction of dendritic spines

Dendritic spines are thin protrusions that cover the dendritic surface of numerous neurons in the brain and whose function seems to play a key role in neural circuits. The correct segmentation of those structures is difficult due to their small size and the resulting spines can appear incomplete. This paper presents a four-step procedure for the complete reconstruction of dendritic spines. The haptically driven procedure is intended to work as an image processing stage before the automatic segmentation step giving the final representation of the dendritic spines. The procedure is designed to allow both the navigation and the volume image editing to be carried out using a haptic device. A use case employing our procedure together with a commercial software package for the segmentation stage is illustrated. Finally, the haptic editing is evaluated in two experiments; the first experiment concerns the benefits of the force feedback and the second checks the suitability of the use of a haptic device as input. In both cases, the results shows that the procedure improves the editing accuracy

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

Patient-specific simulation environment for surgical planning and preoperative rehearsal

Surgical simulation is common practice in the fields of surgical education and training. Numerous surgical simulators are available from commercial and academic organisations for the generic modelling of surgical tasks. However, a simulation platform is still yet to be found that fulfils the key requirements expected for patient-specific surgical simulation of soft tissue, with an effective translation into clinical practice. Patient-specific modelling is possible, but to date has been time-consuming, and consequently costly, because data preparation can be technically demanding. This motivated the research developed herein, which addresses the main challenges of biomechanical modelling for patient-specific surgical simulation. A novel implementation of soft tissue deformation and estimation of the patient-specific intraoperative environment is achieved using a position-based dynamics approach. This modelling approach overcomes the limitations derived from traditional physically-based approaches, by providing a simulation for patient-specific models with visual and physical accuracy, stability and real-time interaction. As a geometrically- based method, a calibration of the simulation parameters is performed and the simulation framework is successfully validated through experimental studies. The capabilities of the simulation platform are demonstrated by the integration of different surgical planning applications that are found relevant in the context of kidney cancer surgery. The simulation of pneumoperitoneum facilitates trocar placement planning and intraoperative surgical navigation. The implementation of deformable ultrasound simulation can assist surgeons in improving their scanning technique and definition of an optimal procedural strategy. Furthermore, the simulation framework has the potential to support the development and assessment of hypotheses that cannot be tested in vivo. Specifically, the evaluation of feedback modalities, as a response to user-model interaction, demonstrates improved performance and justifies the need to integrate a feedback framework in the robot-assisted surgical setting.Open Acces

Augmented reality (AR) for surgical robotic and autonomous systems: State of the art, challenges, and solutions

Despite the substantial progress achieved in the development and integration of augmented reality (AR) in surgical robotic and autonomous systems (RAS), the center of focus in most devices remains on improving end-effector dexterity and precision, as well as improved access to minimally invasive surgeries. This paper aims to provide a systematic review of different types of state-of-the-art surgical robotic platforms while identifying areas for technological improvement. We associate specific control features, such as haptic feedback, sensory stimuli, and human-robot collaboration, with AR technology to perform complex surgical interventions for increased user perception of the augmented world. Current researchers in the field have, for long, faced innumerable issues with low accuracy in tool placement around complex trajectories, pose estimation, and difficulty in depth perception during two-dimensional medical imaging. A number of robots described in this review, such as Novarad and SpineAssist, are analyzed in terms of their hardware features, computer vision systems (such as deep learning algorithms), and the clinical relevance of the literature. We attempt to outline the shortcomings in current optimization algorithms for surgical robots (such as YOLO and LTSM) whilst providing mitigating solutions to internal tool-to-organ collision detection and image reconstruction. The accuracy of results in robot end-effector collisions and reduced occlusion remain promising within the scope of our research, validating the propositions made for the surgical clearance of ever-expanding AR technology in the future

A surgical system for automatic registration, stiffness mapping and dynamic image overlay

In this paper we develop a surgical system using the da Vinci research kit (dVRK) that is capable of autonomously searching for tumors and dynamically displaying the tumor location using augmented reality. Such a system has the potential to quickly reveal the location and shape of tumors and visually overlay that information to reduce the cognitive overload of the surgeon. We believe that our approach is one of the first to incorporate state-of-the-art methods in registration, force sensing and tumor localization into a unified surgical system. First, the preoperative model is registered to the intra-operative scene using a Bingham distribution-based filtering approach. An active level set estimation is then used to find the location and the shape of the tumors. We use a recently developed miniature force sensor to perform the palpation. The estimated stiffness map is then dynamically overlaid onto the registered preoperative model of the organ. We demonstrate the efficacy of our system by performing experiments on phantom prostate models with embedded stiff inclusions.Comment: International Symposium on Medical Robotics (ISMR 2018