| Summary: | Automated medical image analysis has made significant progress over the past decades. With recent advances in acquiring high quality in vivo images of the developing human brain, analysing this data for the purpose of understanding brain development is rapidly becoming feasible. Premature birth increases the risk of developing neurocognitive and neurobehavioural disorders. Studying the morphology and function of the brain during maturation, provides us not only with a better understanding of normal development, but may help identify causes for these. A difficulty is to differentiate between neurodevelopmental consequences and normal variation. Reference models are therefore needed. This thesis presents computational methods used to obtain such models. As a prerequisite, an efficient topology-preserving registration is required. Existing methods have been evaluated mostly on adult brain images, with considerably different shape and appearance. We evaluate approaches for the fast diffeomorphic registration on a publicly available neonatal brain image dataset, and present an improved inverse consistent variant of the stationary velocity free-form deformation algorithm. We employ this algorithm for the construction of a spatio-temporal atlas of the neonatal brain, and compare two different approaches. The first approach is based on the registration of all pairs of images. Residual misalignment thereby still impacts the sharpness of the atlas. More detail is preserved with an iterative refinement of the transformations relating each image to the atlas space. We developed a second approach, which jointly estimates mean shape and longitudinal change iteratively. The final atlas demonstrates increased sharpness and temporal consistency. Finally, we present deformable models for the reconstruction of the neonatal cortex, which correct for common errors observed in state-of-the-art neonatal brain segmentations. Our models were found by experts to be superior to the original segmentation in terms of accurately delineating the cortical anatomy, and form a vital component of image processing pipelines of the Developing Human Connectome Project.
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