Robust estimation for brain tumor segmentation

Given models for healthy brains, tumor segmentation can be seen as a process of detecting abnormalities or outliers that are present with certain image intensity and geometric properties. In this paper, we propose a method that segments brain tumor and edema in two stages. We first detect intensity outliers using robust estimation of the location and dispersion of the normal brain tissue intensity clusters. We then apply geometric and spatial constraints to the detected abnormalities or outliers. Previously published tumor segmentation methods generally rely on the intensity enhancement in the T1-weighted image that appear with the gadolinium contrast agent, on strictly uniform intensity patterns and most often on user initialization of the segmentation. To our knowledge, none of the methods integrated the detection of edema in addition to tumor as a combined approach, although knowledge of the extent of edema is critical for planning and treatment. Our method relies on the information provided by the (non-enhancing) T1 and T2 image channels, the use of a registered probabilistic brain atlas as a spatial prior, and the use of a shape prior for the tumor/edema region. The result is an efficient, automatic segmentation method that defines both, tumor and edema.