Fully automated MRI-based analysis of the locus coeruleus in aging and Alzheimer's disease dementia using ELSI-Net.
Authors
Affiliations (17)
Affiliations (17)
- Department of Neurology Otto von Guericke University Magdeburg (OvGU) Magdeburg Germany.
- Faculty of Computer Science OvGU Magdeburg Germany.
- Institute of Cognitive Neurology and Dementia Research OvGU Magdeburg Germany.
- German Center for Neurodegenerative Diseases (DZNE), Bonn Bonn Germany.
- Department for Cognitive Disorders and Old Age Psychiatry University Hospital Bonn Bonn Germany.
- Department of Psychiatry Medical Faculty University of Cologne Cologne Germany.
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD) University of Cologne Cologne Germany.
- German Center for Neurodegenerative Diseases (DZNE) Magdeburg Germany.
- Department for Psychiatry and Psychotherapy University Clinic Magdeburg Magdeburg Germany.
- German Center for Neurodegenerative Diseases (DZNE) Rostock Germany.
- Department of Psychosomatic Medicine Rostock University Medical Center Rostock Germany.
- Department of Neurology University of Bonn Bonn Germany.
- Luxembourg Centre for Systems Biomedicine (LCSB) University of Luxembourg Esch-sur-Alzette Luxembourg.
- Department of Psychology University of Innsbruck Innsbruck Austria.
- Institute of Cognitive Neuroscience University College London London UK.
- Center for Behavioral Brain Sciences Universitätsplatz 2 Magdeburg Germany.
- Peter L. Reichertz Institute for Medical Informatics Hannover Medical School Hannover Germany.
Abstract
The locus coeruleus (LC) is linked to the development and pathophysiology of neurodegenerative diseases such as Alzheimer's disease (AD). Magnetic resonance imaging-based LC features have shown potential to assess LC integrity in vivo. We present a deep learning-based LC segmentation and feature extraction method called Ensemble-based Locus Coeruleus Segmentation Network (ELSI-Net) and apply it to healthy aging and AD dementia datasets. Agreement to expert raters and previously published LC atlases were assessed. We aimed to reproduce previously reported differences in LC integrity in aging and AD dementia and correlate extracted features to cerebrospinal fluid (CSF) biomarkers of AD pathology. ELSI-Net demonstrated high agreement to expert raters and published atlases. Previously reported group differences in LC integrity were detected and correlations to CSF biomarkers were found. Although we found excellent performance, further evaluations on more diverse datasets from clinical cohorts are required for a conclusive assessment of ELSI-Net's general applicability. We provide a thorough evaluation of a fully automatic locus coeruleus (LC) segmentation method termed Ensemble-based Locus Coeruleus Segmentation Network (ELSI-Net) in aging and Alzheimer's disease (AD) dementia.ELSI-Net outperforms previous work and shows high agreement with manual ratings and previously published LC atlases.ELSI-Net replicates previously shown LC group differences in aging and AD.ELSI-Net's LC mask volume correlates with cerebrospinal fluid biomarkers of AD pathology.