METHOD FOR SEGMENTING AND PREDICTING TISSUE REGIONS IN PATIENTS WITH ACUTE CEREBRAL ISCHEMIA

摘要

A segmentation/prediction method is described for differentiating between infarct, penumbra and healthy regions in a tomographic (e.g. MRI or CT) image dataset of the brain of a stroke patient under examination. The method comprises deriving (7, 11) a multidimensional set of feature vectors from a plurality of baseline modalities, where the modalities comprising both structural and functional modalities. For each volume element of image dataset, an n-dimensional feature vector is extracted (8, 12), such that it represents both structural and functional modalities of the volume element. A classification (13) is performed on the volume element and the classification is used to inform the segmentation (14) in order to label the volume element as belonging to healthy tissue, penumbra tissue, or infarct tissue. The classification operation (13) uses a learning-based classifier, trained using pre- treatment image datasets comprising a plurality of second hypoxic regions, the second hypoxic regions being of the brains of previous stroke patients. In a second embodiment, follow-up (post-treatment) image datasets are used for training the classifier.