With the increasing popularity of robotic surgery, several studies in the literature have investigated automatically assessing skill measures based on motion and video data captured from these systems. A range of simulation environments for robotic surgery are now in development. Skill assessment in these environments has so far only focused on evaluating the utility and validity of statistics such as task completion time, and instrument distance measured during a simulated task. We present the first work using motion data from a robotic surgery simulation environment in development for classifying users of varying skills and detecting completion of trainee. Given the standardized environment of the simulator, and the availability of the ground truth, skill measurements and feedback based on task motion hold the promise of effective automated objective assessment. Based on motion data of a simulated manipulation task from 17 users of varying skills, we demonstrate binary classification (proficient vs. trainee) of user skill with 87.5% accuracy. Alternate measures based on instrument pose more relevant in the simulated environment including a new measure of motion efficiency are also presented and evaluated.

Commercial telerobotic surgery systems for soft tissue surgery are generally limited to visual imaging, though it is possible to simultaneously view picture-in-picture visualization of another workstation. However, it is not easy to correlate such information with the primary endoscopic view since it may not relate to the surface visible in the visual endoscopic images. As critical surfaces as well as surgical targets often lie subsurface, a range of techniques (e.g. ultrasound and near-infrared imaging) and registration methods have been investigated as robotic surgery gains popularity. While investigation of nerves, blood vessels, and tumors has received prior attention, we present a new prototype system for real-time multimodal image registration that focuses on the visualization of the urinary tract. By providing an accurate registration between stereo video images and a near infrared imager, we aim to enhance surgical awareness and make critical uretary tasks such as mobilization of the ureters easier. Engineering validation experiments with a prototype imager, and in-vivo experiments using a prototype Hamamatsu Photodynamic Eye (PDE) imager together with the da Vinci surgical system demonstrating feasibility are presented. These initial experiments have also shown encouraging response from the clinicians.

Robotic surgery is increasingly popular for a wide range of complex minimally invasive surgery procedures. To improve robotic surgery training, a skills trainer simulator called dV-Trainer has recently been introduced, and a da Vinci Skills Simulator is in advanced evaluation. These platforms report a range of time and motion based task metrics and literature has investigated the validity of these metrics in training studies. However, the lack of a cross-platform data collection system has so far prevented a cross-platform investigation. Using a new architecture for collecting cross-platform motion data, we present the first study investigating whether metrics previously validated in simulation environments also hold in training exercises with a real robotic system. Preliminary experiments for an anastomosis needle throwing task in both simulated and real robotic environments are presented, and corresponding performance metrics for both proficient and trainee users are reported.

This paper presents a novel system for image matching in optical endoscopy. The proposed metamatching system approaches the challenge of matching images in a complex scene by incorporating multiple matchers and a decision function. Experiments are presented for Crohn's disease lesion matching in capsule endoscopy with a metamatcher consisting of five independent matchers. We compare the performance of six different types of decision functions. Results show that the F-measure of the metamatching system containing all five matchers is 4%-7% greater than the performance of using the best matcher only, with a maximum F-measure of 0.811. The robustness of the method is validated using simulated data generated by controlled deformations of the image. We also demonstrate how the addition of simulated data to the training set can be used to augment the performance of the metamatcher by up to 10%.

Capsule endoscopy (CE) is aimed at diagnosing disease in areas of the gastrointestinal (GI) tract beyond the reach of conventional endoscopy. Recent work has addressed various methods for reducing the complexity of CE diagnosis and the time needed for analyzing the data. This includes detection of lumen and its contractions, fluids such as blood and intestinal juices, as well as extraneous matter such as food and bubbles. This paper outlines our ongoing work to segment lesions (in particular Crohn's disease) and other abnormalities in CE images. In particular, here we describe the data collection and clinical analysis for our project and preliminary results for segmenting abnormal and extraneous images from a set of 10 CE studies.

This paper reports preliminary experiments with a cooperative robot system to augment single cell manipulation tasks. The JHU "Steady-Hand" robot configuration for cell manipulation is reported. Stable force control laws for the "Steady Hand" are revisited. Preliminary experiments validating stable insertion of a micropipette in a mouse embryo are detailed along with formulation of vision based tracking and augmentation. These preliminary experiments demonstrate promise of cooperative augmentation in single cell manipulation tasks.

Reports preliminary experiments with a robot system designed to cooperatively extend a human's ability to perform fine manipulation tasks requiring human judgement, sensory integration and hand-eye coordination. A completed steady-hand robot is reported. A stable force control law is reviewed. Preliminary experiments validate theoretical predictions of stable one-dimensional control of tool-tip forces in contact with both linearly and nonlinearly compliant objects. Preliminary feasibility experiments demonstrate stable one-dimensional robotic augmentation and "force scaling" of a human operator's tactile input.

This paper describes an ongoing project to develop a computer-integrated system to assist surgeons in revision total hip replacement (RTHR) surgery. In RTHR surgery, a failing orthopedic hip implant, typically cemented, is replaced with a new one by removing the old implant, removing the cement and fitting a new implant into an enlarged canal broached in the femur. RTHR surgery is a difficult procedure fraught with technical challenges and a high incidence of complications. The goals of the computer-based system are the significant reduction of cement removal labor and time, the elimination of cortical wall penetration and femur fracture, the improved positioning and fit of the new implant resulting from precise, high-quality canal milling and the reduction of bone sacrificed to fit the new implant. Our starting points are the ROBODOC system for primary hip replacement surgery and the manual RTHR surgical protocol. We first discuss the main difficulties of computer-integrated RTHR surgery and identify key issues and possible solutions. We then describe possible system architectures and protocols for preoperative planning and intraoperative execution. We present a summary of methods and preliminary results in CT image metal artifact removal, interactive cement cut-volume definition and cement machining, anatomy-based registration using fluoroscopic X-ray images and clinical trials using an extended RTHR version of ROBODOC. We conclude with a summary of lessons learned and a discussion of current and future work.