A computational ontology framework for the synthesis of multi-level pathology reports from brain MRI scans

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.

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Publication Details

Journal
Journal of Alzheimer s Disease
Published
2025-04-21
DOI
https://doi.org/10.1177/13872877251331222
Citations
3
Primary Topic
Biomedical Text Mining and Ontologies
Type
article
Field-Weighted Citation Impact
2.42

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article

A computational ontology framework for the synthesis of multi-level pathology reports from brain MRI scans

Andrea Lohse, Silka Dawn Freiesleben, Jens Wiltfang, Alexander Bernhardt et al.
3 citations
Journal of Alzheimer s Disease
Biomedical Text Mining and Ontologies
2.42
article

A computational ontology framework for the synthesis of multi-level pathology reports from brain MRI scans

Andrea Lohse, Silka Dawn Freiesleben, Jens Wiltfang, Alexander Bernhardt, Ayda Rostamzadeh, Lukas Preis, Falk Lüesebrink, Robert Perneczky, Annika Spottke, Oliver Peters, Stefan Johannes Teipel, Michael Ewers, Matthis Synofzik, Boris‐Stephan Rauchmann, Devesh Singh, Johannes Levin, Klaus Scheffler, Alice Grazia, Christoph Laske, Katharina Büerger, Klaus Fließbach, Martin Dyrba, Josef Priller, Andreas Hermann, Okka Kimmich, Achim Reiz, Stefan Hetzer, Daniel Janowitz, Johannes Prudlo, Ingo Kilimann, Anja Schneider, Michaela Butryn, Slawek Altenstein, Luisa‐Sophie Schneider, Wenzel Glanz, Matthias H. Munk, Björn H. Schott, Nina Roy‐Kluth, Lukas Beichert, Peter Dechent, Eike Jakob Spruth, Emrah Duezel, Frank Jessen
article en
3 citations

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.

Journal of Alzheimer s DiseaseVol. 108(1_suppl)
University of Bonn (DE), University of Cologne (DE), University Hospital Bonn (DE), German Center for Neurodegenerative Diseases (DE), LMU Klinikum (DE), Munich Cluster for Systems Neurology (DE), University Children's Hospital Tübingen (DE), UK Dementia Research Institute (GB), Cologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases (DE), University Hospital Magdeburg (DE), Hertie Institute for Clinical Brain Research (DE), University of Rostock (DE), Imperial College London (GB), University of Aveiro (PT), Technical University of Munich (DE), University of Göttingen (DE), Charité - Universitätsmedizin Berlin (DE), University of Tübingen (DE), Ludwig-Maximilians-Universität München (DE), University of Sheffield (GB), Leibniz Institute for Neurobiology (DE), University of Edinburgh (GB)
Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 12%
Biomedical Text Mining and Ontologies
2.42
DOIOpen Access PDFOpenAlex
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