Collections > Electronic Theses and Dissertations > An MRI Segmentation Framework for Brains with Anatomical Deviations

The segmentation of brain Magnetic Resonance (MR) images, where the brain is partitioned into anatomical regions of interest, is a notoriously difficult problem when the underlying brain structures are influenced by pathology or are undergoing rapid development. This dissertation proposes a new automatic segmentation method for brain MRI that makes use of a model of a homogeneous population to detect anatomical deviations. The chosen population model is a brain atlas created by averaging a set of MR images and the corresponding segmentations. The segmentation method is an integration of robust parameter estimation techniques and the Expectation-Maximization algorithm. In clinical applications, the segmentation of brains with anatomical deviations from those commonly observed within a homogeneous population is of particular interest. One example is provided by brain tumors, since delineation of the tumor and of any surrounding edema is often critical for treatment planning. A second example is provided by the dynamic brain changes that occur in newborns, since study of these changes may generate insights into regional growth trajectories and maturation patterns. Brain tumor and edema can be considered as anatomical deviations from a healthy adult population, whereas the rapid growth of newborn brains can be considered as an anatomical deviation from a population of fully developed infant brains. A fundamental task associated with image segmentation is the validation of segmentation accuracy. In cases in which the brain deviates from standard anatomy, validation is often an ill-defined task since there is no knowledge of the ground truth (information about the actual structures observed through MRI). This dissertation presents a new method of simulating ground truth with pathology that facilitates objective validation of brain tumor segmentations. The simulation method generates realistic-appearing tumors within the MRI of a healthy subject. Since the location, shape, and volume of the synthetic tumors are known with certainty, the simulated MRI can be used to objectively evaluate the accuracy of any brain tumor segmentation method.