A decision-
support system and computer implemented method automatically measures the midline shift in a patient's brain using
Computed Tomography (CT) images. The decision-
support system and computer implemented method applies
machine learning methods to features extracted from multiple sources, including midline shift, blood amount, texture pattern and other injury data, to provide a physician an estimate of
intracranial pressure (ICP) levels. A hierarchical segmentation method, based on
Gaussian Mixture Model (GMM), is used. In this approach, first an Magnetic
Resonance Image (MRI)
ventricle template, as prior knowledge, is used to estimate the region for each
ventricle. Then, by matching the
ventricle shape in CT images to the MRI ventricle template set, the corresponding MRI slice is selected. From the
shape matching result, the feature points for midline
estimation in CT slices, such as the center edge points of the
lateral ventricles, are detected. The amount of shift, along with other information such as
brain tissue texture features, volume of blood accumulated in the brain,
patient demographics, injury information, and features extracted from physiological signals, are used to
train a
machine learning method to predict a variety of important clinical factors, such as
intracranial pressure (ICP), likelihood of success a particular treatment, and the need and / or dosage of particular drugs.