Abstract. Analysis of 4D medical images presenting pathology (i.e., le-sions) is significantly challenging due to the presence of complex changes over time. Image analysis methods for 4D images with lesions need to account for changes in brain structures due to deformation, as well as the formation and deletion of new structures (e.g., edema, bleeding) due to the physiological processes associated with damage, intervention, and recovery. We propose a novel framework that models 4D changes in pathological anatomy across time, and provides explicit mapping from a healthy template to subjects with pathology. Moreover, our frame-work uses transfer learning to leverage rich information from a known source domain, where we have a collection of completely segmented im-ages, to yield effective appearance models for the input target domain. The automatic 4D segmentation method uses a novel domain adapta-tion technique for generative kernel density models to transfer informa-tion between different domains, resulting in a fully automatic method that requires no user interaction. We demonstrate the effectiveness of our novel approach with the analysis of 4D images of traumatic brain injury (TBI), using a synthetic tumor database as the source domain. 1

Abstract. Neurodegenerative diseases such as Alzheimer's disease present subtle anatomical brain changes before the appearance of clinical symptoms. Large longitudinal brain imaging datasets are now accessible to investigate these structural changes over time. However, manual structure segmentation is long and tedious and although automatic methods exist, they are often performed in a cross-sectional manner where each visit is analysed independently. With such analysis methods, bias, error and longitudinal noise may be introduced. Noise due to MR scanners and other physiological effects may also introduce variability in the measurement. We propose to use 4D non-linear registration with spatio-temporal regularization to correct for longitudinal inconsistency in the context of structure segmentation. The major contribution of this article is the individual template creation with spatio-temporal regularization of the deformation fields for each subject. We validate our method with different sets of real MRI data and demonstrate that spatially local temporal regularization yields more consistent rates of change of global structures resulting in better statistical power for detecting significant changes occurring between populations.

Medical imaging studies increasingly use longitudinal images of individual subjects in order to follow-up changes due to development, degeneration, disease progression or efficacy of therapeutic intervention. Repeated image data of individuals are highly correlated, and the strong causality of information over time lead to the development of procedures for joint seg-mentation of the series of scans, called 4D segmentation. A main aim was improved consistency of quantitative analysis, most often solved via patient-specific atlases. Challenging open problems are contrast changes and occurance of sub-classes within tissue as observed in multimodal MRI of infant development, neurodegeneration and disease. This paper pro-poses a new 4D segmentation framework that enforces con-tinuous dynamic changes of tissue contrast patterns over time as observed in such data. Moreover, our model includes the capability to segment different contrast patterns within a spe-cific tissue class, for example as seen in myelinated and un-myelinated white matter regions in early brain development. Proof of concept is shown with validation on synthetic image data and with 4D segmentation of longitudinal, multimodal pediatric MRI taken at 6, 12 and 24 months of age, but the methodology is generic w.r.t. different application domains using serial imaging. 1.