Surgery is primarily taught through mentoring, where an expert mentor supervises a mentee performing surgery, taking over when necessary. Telementoring systems aim to provide mentees with access to remote mentors, but the physical distance between mentors and mentees poses unique challenges to surgical training. We investigate the underlying needs leading to takeovers in onsite mentoring and assess mentors’ ability to fulfill address these needs remotely using existing telestration tools, namely pointers and drawings on shared views. Through interviews and workshops with expert surgeons, we find that (1) mentors take over to convey gestures related to instrument placement, tissue displacement, force, and movement, (2) mentors gather information about location of tissue, equipment, and instruments, as well as gesture constraints, and (3) surgeons judge telestration insufficient for these needs. Based on this gap between onsite mentoring practices and telementoring tools, we discuss novel tools to address these needs and their evaluation.

All along their careers, surgeons learn through mentoring, operating on patients side-by-side with expert surgeons. However, access to experts is more and more challenging as they are often located far from learners. Telementoring can increase the reach of mentors by supporting mentoring at a distance, but despite their potential, current systems remain rarely institutionalized. We investigate current surgical mentoring practices, to inform future design of telementoring systems in providing support for learning. In a field study, we observe 11 surgeries and conduct follow-up self-confrontation interviews with mentor-mentee pairs. Through Thematic Analysis, we find that the domain of surgery results in mentee's needs for reassurance regarding safety, being spared from workload and risk, both essential to support the acquisition of skills. Second, that mentors understand the situation primarily through the surgical site view but also through inspecting the mentee's posture and the patient's body. Thirdly, that mentors take physical distance as their presence limits mentees' autonomy development. We discuss the limits of current surgical telementoring systems and rethink their role as companions of onsite mentoring.

In the context of developing a pedagogical tool for teaching anatomy, the need for a comparative study between two RGB-D cameras has emerged. This paper addresses the assessment of the accuracy and precision of two RGB-D sensors (Carmine Primesense and Persee Orbbec) through two different experiments. The evaluation not only provides comparative results on the sensors performances but also aims at determining in which conditions they are the most efficient. The first experiment evaluates the variability of the output depth map data. The second experiment focuses on analyzing the influence of the distance in the positioning accuracy of an object submitted to controlled displacements. The results are summarized in a set of error heat maps and a table; they provide clues for using one sensor rather than another by describing their robustness both in a static scene and in a motion capture scenario.

In microsurgery, a surgeon often deals with anatomical structures of sizes that are close to the limit of the human hand accuracy. Robotic assistants can help to push beyond the current state of practice by integrating imaging and robot-assisted tools. This paper demonstrates control of a handheld tremor reduction micromanipulator with visual servo techniques, aiding the operator by providing three behaviors: snap-to, motion-scaling, and standoff-regulation. A stereo camera setup viewing the workspace under high magnification tracks the tip of the micromanipulator and the desired target object being manipulated. Individual behaviors activate in task-specific situations when the micromanipulator tip is in the vicinity of the target. We show that the snap-to behavior can reach and maintain a position at a target with an accuracy of 17.5 ± 0.4μm Root Mean Squared Error (RMSE) distance between the tip and target. Scaling the operator's motions and preventing unwanted contact with non-target objects also provides a larger margin of safety.

We describe a general methodology for tracking 3-dimensional objects in monocular and stereo video that makes use of GPU-accelerated filtering and rendering in combination with machine learning techniques. The method operates on targets consisting of kinematic chains with known geometry. The tracked target is divided into one or more areas of consistent appearance. The appearance of each area is represented by a classifier trained to assign a class-conditional probability to image feature vectors. A search is then performed on the configuration space of the target to find the maximum likelihood configuration. In the search, candidate hypotheses are evaluated by rendering a 3D model of the target object and measuring its consistency with the class probability map. The method is demonstrated for tool tracking on videos from two surgical domains, as well as in a human hand-tracking task.

Certain surgical procedures require a high degree of precise manual control within a very restricted area. Retinal surgeries are part of this group of procedures. During vitreoretinal surgery, the surgeon must visualize, using a microscope, an area spanning a few hundreds of microns in diameter and manually correct the potential pathology using direct contact, free hand techniques. In addition, the surgeon must find an effective compromise between magnification, depth perception, field of view, and clarity of view. Pre-operative images are used to locate interventional targets, and also to assess and plan the surgical procedure. This paper proposes a method of fusing information contained in pre-operative imagery, such as fundus and OCT images, with intra-operative video to increase accuracy in finding the target areas. We describe methods for maintaining, in real-time, registration with anatomical features and target areas using image processing. This registration allows us to produce information enhanced displays that ensure that the retinal surgeon is always in visual contact with his/her area of interest.

Until recently, computer-aided medical interventions (CAMI) and medical robotics have focused on rigid and nondeformable 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