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Publication Detail
Automated segmentation of the locus coeruleus in aging and Alzheimer's disease using 3T neuromelanin-sensitive MRI
  • Publication Type:
    Conference presentation
  • Authors:
    Dünnwald M, Sarkar M, Yakupov R, Spottke A, Hämmerer D, Schneider A, Kilimann I, Teipel SJ, Jessen F, Düzel E, Oeltze-Jafra S, Betts M
  • Date:
    01/12/2021
  • Status:
    Published
Abstract
BACKGROUND: The Locus Coeruleus (LC) may play an important role in the pathogenesis of Alzheimer's disease (AD). Neuromelanin-sensitive Magnetic Resonance Imaging (MRI) permits the visualization of the LC in vivo. Previously, we proposed an automatic, deep learning-based approach to segmenting the LC in a cohort of 82 healthy young and older adults (82 HC; Dünnwald et al. 2021). Here, we applied the same automatic segmentation method to baseline data from the DZNE Longitudinal Cognitive Impairment and Dementia (DELCODE) study comprising 42 healthy elderly adults (HEA), 18 participants with subjective cognitive decline (SCD), 25 participants with mild cognitive impairment (MCI) and 11 participants with AD. METHOD: The LC was visualized using 3T T1-weighted Fast Low Angle Shot (FLASH) MRI acquired at 0.75mm isotropic resolution. Prior to processing, the data was sinc-interpolated to a resolution of 0.375mm³. The automated segmentation pipeline comprises two consecutive Convolutional Neural Networks (CNNs), which perform LC centerpoint regression and segmentation (see Fig. 1). The networks were trained only on the 82 HC dataset using a cross-validation scheme. We applied the networks to the unseen DELCODE data and assessed their performance with respect to manually delineated masks from two trained raters using Dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC) of contrast ratios (CRs). RESULT: In HEA, the networks showed similar agreement (DSC, ICC) compared to that observed between two independent raters. Using DSC, automated segmentation of SCD, MCI and AD groups showed less agreement than in the HEA group with 66.67%, 64.86%, 59.77% and 67.06% on average respectively (see Fig. 2). However, good reliability between automated and manually delineated masks for all groups using LC MRI contrast was observed (see Tab. 1) indicating that CRs may be relatively robust wrt. mask differences (see Fig. 3 and 4 for qualitative comparisons). CONCLUSION: The results demonstrate good reliability between manual and automatically generated LC segmentations with respect to LC MRI contrast using networks trained on healthy older adults. Additional fine-tuning of the networks to account for the variance in AD should be explored as a means to further improve reliability.
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