TELEMEDICINE IN PRACTICE From operating theatre to operating studio -visualising surgery in the age of telemedicine

2 Summary The introduction of communication technology into operating theatres involves significant changes and challenges. We have conducted a telemedicine project between two of Norway's largest hospitals (Rikshospitalet and Ullevaal hospital) with a focus on image-guided surgical and radiological procedures. Video was broadcast using a 34 Mbit/s ATM network. This resulted in changes in the local work practice to accommodate and facilitate the communication. It also required changes in the surgeon's work situation with regard to teaching in order to improve communication with remote viewers. These changes were non-trivial, and we see them as signs of a shift towards a more public kind of surgery and interventional radiology brought about by the technology. Introduction Once an operating theatre was literally a theatre, where dissections and operations were performed for the audience who were seated in the room around the table. This public aspect of surgery has for a long time (and for good reasons) been downplayed and limited to the teaching hospitals or to defined training programmes. However, visualisation of the surgical performance may be re-emerging as telemedicine is introduced into surgery. In this case the audience's presence is virtual, being mediated by the technology. This increases the flexibility immensely, because the audience may be distributed in space (remotely located) or in time (e.g. by broadcasting stored material from a database). The effect is that the operating theatre once more becomes a real "theatre", or perhaps "operating studio" would be an appropriate term

Measuring Shape and Motion of White Blood Cells from Sequences of Fluorescence Microscopy Images

We describe an image analysis system developed to measure the motion of white blood cells from a temporal sequence of fluorescence microscopy images. A two-pass spatio-temporal segmentation system is proposed. Pixels are classified as cell and background pixels by an initial segmentation in the first pass. Region labeling, correction and cell tracking are done in the second pass. After segmentation, shape features are estimated from discrete regions, and cell motion is then measured by using the shape features. A supervised method based on the shape features is used to evaluate the results of the segmentation