Functional outcome, quality of life
The face is a crucial and complex part of the human body that is the centre of a host of important functions with great implications for the direct and indirect survival of the individual. Facial fractures are common in conjunction with motor vehicle accidents, abuse, sports-related accidents and accidents at work. These injuries run the gamut from minor fractures with no or negligible implications, to multiple injuries that damage several vital facial functions resulting in extensive functional impairment.
Each year in Sweden, many people are severely injured in motor vehicle accidents and require hospital care. Among motor vehicle accident survivors, 50–70% sustains facial injuries. In order to restore function and aesthetics and preserve quality of life for these individuals, carefully planned surgery is required and must be carried out with high-level precision. The aim is to conduct in-depth analyses of skull and facial fractures and their ramifications and to create a structure for research and quality assurance in a field with high potential for further development, using a framework of multidisciplinary collaboration between the University Hospital in Uppsala, Sweden, and the University Hospital in Basel, Switzerland.
The project uses newly developed computerized fracture classification systems that define fractures in great detail to facilitate documentation and web-based communication between the involved parties. In addition to classification, function and quality of life are assessed. An important component in surgery planning is to be able to accurately measure the extent of certain anatomical structures. Of particular interest in CMF surgery are the shape and volume of the orbits (eye sockets) comparing the left and right side. These properties can be measured in CT images of the skull, but this requires the orbits to be extracted from the rest of the image, a process called segmentation. Segmentation is usually performed by manual tracing of the orbit in a large number of slices of the CT image. This task is very time consuming, and sensitive to operator errors.
Semi-automatic segmentation methods could reduce the required operator time significantly. A part of the project was to develop and evaluate a semi-automatic system for segmenting the orbit in CT images which has been completed and now applied in evaluation of outcome in a collaborative project. The expected final outcome of the project is a working prototype of such a segmentation system.