This paper presents an approach to semiautomatic segmentation over a time series without the use of a priori geometric anatomical knowledge, and demonstrates its applicability to pericardial effusion segmentation from ultrasound. The described technique solves the problem in two stages, first automatically calculating a set of exclusion zones, then leveraging the surgeon’s anatomical knowledge, simply and interactively, to create a region which corresponds to the stable region within the target effusion. In preliminary testing, the system performs well versus manual segmentation, outperforming it both in terms of perceived quality, as measured in a blinded comparison by an expert, and in terms of time required for generation.

In the field of percutaneous access to soft tissues, our project was to improve classical pericardiocentesis by performing accurate guidance to a selected target, according to a model of the pericardial effusion acquired through three-dimensional (3D) data recording. Required hardware is an echocardiographic device and a needle, both linked to a 3D localizer, and a computer. After acquiring echographic data, a modeling procedure allows definition of the optimal puncture strategy, taking into consideration the mobility of the heart, by determining a stable region, whatever the period of the cardiac cycle. A passive guidance system is then used to reach the planned target accurately, generally a site in the middle of the stable region. After validation on a dynamic phantom and a feasibility study in dogs, an accuracy and reliability analysis protocol was realized on pigs with experimental pericardial effusion. Ten consecutive successful punctures using various trajectories were performed on eight pigs. Nonbloody liquid was collected from pericardial effusions in the stable region (5 to 9 mm wide) within 10 to 15 minutes from echographic acquisition to drainage. Accuracy of at least 2.5 mm was demonstrated. This study demonstrates the feasibility of computer-assisted pericardiocentesis. Beyond the simple improvement of the current technique, this method could be a new way to reach the heart or a new tool for percutaneous access and image-guided puncture of soft tissues. Further investigation will be necessary before routine human application.

Until now, computer assisted surgery has focused primarily on surgical procedures involving rigid anatomical structures. Because soft tissues can be highly mobile and deformable, they may require specific imaging devices, suitable modeling tools, and guiding systems. Percutaneous pericardial puncture is a good clinical target for computer assisted surgery; this procedure is often performed without direct visualization and is dangerous even though echographic control is used. Computer assistance can greatly improve this technique and will allow accurate puncture of preplanned targets. This paper describes a new approach for computer assisted pericardial punctures (CASPER) and describes a first feasibility analysis of CASPER demonstrated with anesthetized animals. The approach is based on the use of echographic data localized in space, from which an optimal strategy is defined. Because of the specificity of the pericardial effusion, a stable target can be selected despite the heart motions. A passive guiding system is used. We have demonstrated the feasibility of the approach.

Until now, Computer Assisted Surgery has focused mostly on surgical procedures for rigid anatomical structures. Because soft tissues may be highly mobile and deformable, they may require specific imaging devices, suitable modelling tools and guiding systems. Percutaneous pericardial puncture is a good clinical target: this procedure is often blind and dangerous even though echographic control is used. Using computer assistance can greatly improve this technique and will allow accurate puncture of pre-planned targets. This paper describes a new approach for Computer ASsisted PERicardial punctures and describes a first feasibility analysis of GASPER demonstrated with anesthetized dogs.

Passive and semi-active aids ince 1985, a team of computer scien-surgeons has been involved in a project at Grenoble Hospital called computer assisted medical interventions (CAMI). The aim of the project is to help surgeons and physicians use multimodal data in a rational and quantitative way in order to plan and to perform medical interventions. Recent advances in medical imaging systems such as CT and MRI have stimulated research on the interpretation of medical images. Nevertheless, very few systems allow for an efficient therapeutic use of the wealth of information these images contain , which is CAMI's twofold objective: =Define an operative strategy that takes advantage of the localizing capabilities of imaging, and make this strategy available in an operative reference system. This goal requires models and processing of basic data, in conjunction with a priori knowledge , in order to define an optimal strategy. =Perform the previously defined operative strategy, with the aid of a suitable guidance system, under appropriate imaging supervision. Without help, executing a strategy can sometimes be very difficult. First, reproducing a defined strategy directly raises problems, for one has to mentally match geometrical information observed in different reference systems (mainly intra-operative scenes or images with pre-operative images). Then, complex interventions may be necessary on organs difficult to reach or for which a human operator will only have poor or no visibility. In addition, surgical tools, such as probes, most often have to be very precisely positioned, and submillimetric accuracy may be required for microsurgery. Finally, some interventions may be dangerous for the medical staff (e.g., contamination or irradiation). In all these instances, optical or mechanical guidance systems are required. The ultimate success of this research hinges on complex robotics systems and their various sensors. The aforementioned objectives aim at improving the quality of the interventions by making it easier, more accurate, closer to a a pre-operative simulation where accurate objectives can be defined, and sometimes faster. Also, it may be possible to devise new interventions and to validate protocols of therapeutic research. Obviously , this is long term research, with many potential clinical applications. Yet, a general methodology can be applied to various clinical situations [ 11, as presented below. In order to understand the methodology and applications of CAMI, it is necessary to be aware of current technology. Many issues in the CAMI project are concerned with geometrical localization problems. Hardware issues mainly refer …