A specific challenge to accurate tissue quantification in premature neonatal MRI data is posed by Intra-Ventricular Hemorrhage (IVH), where severe cases can be accompanied by extreme and complex Ventriculomegaly (VM). IVH is apparent on MRI as bright signal pooling within the ventricular space in locations related to the original bleed and how the blood pools and clots due to gravity. High variability in the location and extent of IVH and in the shape and size of the ventricles due to ventriculomegaly (VM), combined with a lack of large sets of training images covering all possible configurations, mean it is not feasible to approach the problem using whole brain dictionary learning. Here, we propose a novel sparse dictionary approach that utilizes a spatial dictionary for normal tissues structures, and a non-spatial component to delineate IVH and VM structure. We examine the behavior of this approach using a dataset of premature neonatal MRI scans with severe IVH and VM, finding improvements in the segmentation accuracy compared to the conventional segmentation. This approach provides the first automatic whole-brain segmentation framework for severe IVH and VM in premature neonatal brain MRIs.
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