Volume-of-interest segmentation of cortical regions for multimodal brain analysis

Multimodal tomographic images (e.g., MRI, PET images) provide important information for research, diagnosis and therapy of brain diseases. Structural and functional properties and changes in cortical brain regions are often examined quantitatvely in certain volumes of interest (VOIs) bounded by tissue borders and cortical sulci as important VOI borders. Thus, VOI segmentation is an important prerequisite for this kind of brain analysis. In order to avoid time-consuming slice-by-slice segmentation of VOIs, the new semi-automatic method aims at segmenting cortical VOIs based on 3D brain surface visualization and thus defining VOI borders along cortical sulci much easily than in successive 2D slices. The method was evaluated based on phantoms with simulated sulcus and class properties and real brain data sets. Promising results were obtained regarding segmentation accuracy and computation time.