Abstract
Alzheimer’s disease (AD) accounts for more than two-thirds of all dementia cases. Existing MRI volumetry tools summarize pathology found within brain MRI scans. However, they often lack methods for aggregating information at different brain abstraction levels, and lack an intuitive visualizations.We propose a computational pipeline for quantifying hierarchical volumetric deviations and generating interactive summary visualizations. We collected N=3115 MRI scans from five different data cohorts. We used the FastSurferCNN tool to obtain brain region segmentations and estimate their raw volumes. First, we created a semantic model, encoding hierarchical anatomical relationships in the web ontology language (OWL) model and a computational framework for aggregating volumetric deviations. Second,we developed a visualization framework, providing interactive visual ‘sunburst’ summary plots. The summary plots can highlight mean-group or single-subject atrophy profiles, enhancing visual comparison of atrophy profiles with different AD phases. Our pipeline could assist clinicians in discovering brain pathologies or subgroups in an interpretable and reliable manner.
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© 2024 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Singh, D., Dyrba, M. (2024). Computational Ontology and Visualization Framework for the Visual Comparison of Brain Atrophy Profiles. In: Maier, A., Deserno, T.M., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2024. BVM 2024. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-44037-4_43
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DOI: https://doi.org/10.1007/978-3-658-44037-4_43
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