A computational ontology framework for the synthesis of multi-level pathology reports from brain MRI scans
Year: 2025
Type: article
Abstract: BackgroundConvolutional neural network (CNN) based volumetry of MRI data can help differentiate Alzheimer's disease (AD) and the behavioral variant of frontotemporal dementia (bvFTD) as causes of cognitive decline and dementia. However, existing CNN-based MRI volumetry tools lack a structured hierarchical representation of brain anatomy, which would allow for aggregating regional pathological information and automated computational inference.ObjectiveDevelop a computational ontology pipeline for quantifying hierarchical pathological abnormalities and visualize summary charts for brain atrophy findings, aiding differential diagnosis.MethodsUsing FastSurfer, we segmented brain regions and measured volume and cortical thickness from MRI scans pooled across multiple cohorts (N = 3433; ADNI, AIBL, DELCODE, DESCRIBE, EDSD, and NIFD), including healthy controls, prodromal and clinical AD cases, and bvFTD cases. Employing the Web Ontology Language (OWL), we built a semantic model encoding hierarchical anatomical information. Additionally, we created summary visualizations based on sunburst plots for visual inspection of the information stored in the ontology.ResultsOur computational framework dynamically estimated and aggregated regional pathological deviations across different levels of neuroanatomy abstraction. The disease similarity index derived from the volumetric and cortical thickness deviations achieved an AUC of 0.88 for separating AD and bvFTD, which was also reflected by distinct atrophy profile visualizations.ConclusionsThe proposed automated pipeline facilitates visual comparison of atrophy profiles across various disease types and stages. It provides a generalizable computational framework for summarizing pathologic findings, potentially enhancing the physicians' ability to evaluate brain pathologies robustly and interpretably.
Source: Journal of Alzheimer s Disease
Authors: Devesh Singh, Alice Grazia, Achim Reiz, Andreas Hermann, Slawek Altenstein, Lukas Beichert, Alexander Bernhardt, Katharina Buerger, Michaela Butryn, Peter Dechent, Emrah Duezel, Michael Ewers, Klaus Fliessbach, Silka D Freiesleben, Wenzel Glanz, Stefan Hetzer, Daniel Janowitz, Ingo Kilimann, Okka Kimmich, Christoph Laske, Johannes Levin, Andrea Lohse, Falk Luesebrink, Matthias Munk, Robert Perneczky, Oliver Peters, Lukas Preis, Josef Priller, Johannes Prudlo, Boris S Rauchmann, Ayda Rostamzadeh, Nina Roy-Kluth, Klaus Scheffler, Anja Schneider, Luisa S Schneider, Björn H Schott, Annika Spottke, Eike J Spruth, Matthis Synofzik, Jens Wiltfang, Frank Jessen, Stefan J Teipel, Martin Dyrba, ADNI, AIBL, FTLDNI, study groups
Institutions: German Center for Neurodegenerative Diseases, University of Rostock, Universitätsmedizin Rostock, Charité - Universitätsmedizin Berlin, Hertie Institute for Clinical Brain Research, University of Tübingen, LMU Klinikum, Ludwig-Maximilians-Universität München, Institut für Schlaganfall- und Demenzforschung, University Hospital Magdeburg, University of Göttingen, University of Bonn, Universitätsklinikum Tübingen, Munich Cluster for Systems Neurology, Imperial College London, UK Dementia Research Institute, Technical University of Munich, University of Edinburgh, University of Sheffield, University of Cologne, Universitätsmedizin Göttingen, Leibniz Institute for Neurobiology, University Hospital Bonn, University of Aveiro, Cologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4409620587