3D norm data: the first step towards semiautomatic virtual craniofacial surgery.

When planning craniofacial surgical interventions, the ideal appearance of the patient is very important. The final appearance should be as close as possible to that which the patient would have if he/she were without defects. Our first step towards achieving this is to build a database containing sets of three-dimensional CT images that allows for comparison of the shape of a patient with defects to the typical shape of an age- and sex-matched "average" person without defects.

Computer aided planning device for preoperative bending of osteosynthesis plates.

In craniofacial surgery bone fractures and repositioned bone segments often have to be fixed by titanium miniplates. In clinical routines the surgeon has to fit each miniplate t be used to the individual bone structure of the patient: bending and fitting of a miniplate must frequently be repeated several times. Often up to twenty minutes are required to achieve the best fit of a single osteosynthesis plate.

A real-time CORBA based system architecture for robot assisted craniofacial surgery.

We present the concept of a system architecture for the computer aided craniofacial surgery. The architecture is based on CORBA, an industrial standard specification for the development of distributed applications. Our concept includes a fundamental behaviour oriented communication model and some fundamental software safety considerations.

[Robotics in oral and maxillofacial surgery. Possibilities, chances, risks].

Robot systems are being tested in stereotactic neurosurgical interventions, orthopedic surgery of the hip or knee and advancal endoscopic systems for minimally invasive surgery. In contrast to most industrially manufactured products, objects for medical treatment are characterized by plasticity as well as by complex and individual forms. Thus, features of robots in this field have to be further developed in terms of advanced sensory and specific micromotoric systems.

Knowledgebased segmentation.

The segmentation of medical images like CT or MRI scans represents a great challenge to researchers in computer vision, due to the variability of the individual anatomy and the different characteristics of the scanning systems. As an anatomical knowledge base improves the recognition of structures in CT or MRI scans, we chose a knowledge based segmentation in our approach, which will be explained in the following.