Problems of interactive segmentation.

Computer-assisted surgery makes a patient-individual treatment feasible, aiming at decreased surgical risk and reduced recovery time of patients. At present, in areas of application, e.g., heart surgery as well as craniofacial surgery, its use is still limited to complex cases due to the high effort. In surgical planning, it is caused by extensive medical image analysis, including tissue classification. Especially, the classification (or segmentation) requires a lot of manual intervention. For a long time research has been devoted solely to computational aspects of segmentation, where usability aspects has been out of scope. This article focuses on the major problems of interactive segmentation and provides consequences on the segmentation process towards a solution.

The impact of 3-dimensional reconstructions on operation planning in liver surgery.

BACKGROUND: Operation planning in liver surgery depends on the precise understanding of the 3-dimensional (D) relation of the tumor to the intrahepatic vascular trees. To our knowledge, the impact of anatomical 3-D reconstructions on precision in operation planning has not yet been studied. HYPOTHESIS: Three-dimensional reconstruction leads to an improvement of the ability to localize the tumor and an increased precision in operation planning in liver surgery. DESIGN: We developed a new interactive computer-based quantitative 3-D operation planning system for liver surgery, which is being introduced to the clinical routine. To evaluate whether 3-D reconstruction leads to improved operation planning, we conducted a clinical trial. The data sets of 7 virtual patients were presented to a total of 81 surgeons in different levels of training. The tumors had to be assigned to a liver segment and subsequently drawn together with the operation proposal into a given liver model. The precision of the assignment to a liver segment according to Couinaud classification and the operation proposal were measured quantitatively for each surgeon and stratified concerning 2-D and different types of 3-D presentations. RESULTS: The ability of correct tumor assignment to a liver segment was significantly correlated to the level of training (P<.05). Compared with 2-D computed tomography scans, 3-D reconstruction leads to a significant increase of precision in tumor localization by 37%. The target area of the resection proposal was improved by up to 31%. CONCLUSION: Three-dimensional reconstruction leads to a significant improvement of tumor localization ability and to an increased precision of operation planning in liver surgery.

An architecture for implementing customizable medical image processing systems.

Monolithic image processing systems containing a superset of imaging algorithms are difficult to use and require specialized knowledge of image processing. Thus they increase the workload of medical personnel instead of making the work situation easier. Customizable medical image processing systems on the other hand may be easily adapted to address various problems in the medical image processing domain integrating only the necessary subset of image processing functionality presented in on intuitive way. In this work we present an architecture for creating customizable image processing systems for the medical domain. We address three major topics: 1.) easy, goal-oriented customization of imaging systems by using a generalized algorithm model and repository, 2.) dynamic, data-oriented parameterization of the selected algorithms and 3.) semi-automated generation of user interface components for each new algorithm to be inserted in an imaging system based on cognitive ergonomics. We conclude with the presentation of an initial implementation of the architecture in form of an object-oriented framework for the creation of components for customizable medical imaging systems.