Purpose
Development and performance measurement of a fully automated pipeline that localizes and segments the locus coeruleus in so-called neuromelanin-sensitive magnetic resonance imaging data for the derivation of quantitative biomarkers of neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease.
Methods
We propose a pipeline composed of several 3D-Unet-based convolutional neural networks for iterative multi-scale localization and multi-rater segmentation and non-deep learning-based components for automated biomarker extraction. We trained on the healthy aging cohort and did not carry out any adaption or fine-tuning prior to the application to Parkinson’s disease subjects.
Results
The localization and segmentation pipeline demonstrated sufficient performance as measured by Euclidean distance (on average around 1.3mm on healthy aging subjects and 2.2mm in Parkinson’s disease subjects) and Dice similarity coefficient (overall around $$71\%$$
71
%
on healthy aging subjects and $$60\%$$
60
%
for subjects with Parkinson’s disease) as well as promising agreement with respect to contrast ratios in terms of intraclass correlation coefficient of $$\ge 0.80$$
≥
0.80
for healthy aging subjects compared to a manual segmentation procedure. Lower values ($$\ge 0.48$$
≥
0.48
) for Parkinson’s disease subjects indicate the need for further investigation and tests before the application to clinical samples.
Conclusion
These promising results suggest the usability of the proposed algorithm for data of healthy aging subjects and pave the way for further investigations using this approach on different clinical datasets to validate its practical usability more conclusively.
The locus coeruleus (LC) is a small brain structure in the brainstem that may play an important role in the pathogenesis of Alzheimer's Disease (AD) and Parkinson's Disease (PD). The majority of studies to date have relied on using manual segmentation methods to segment the LC, which is time consuming and leads to substantial interindividual variability across raters. Automated segmentation approaches might be less error-prone leading to a higher consistency in Magnetic Resonance Imaging (MRI) contrast assessments of the LC across scans and studies. The objective of this study was to investigate whether a convolutional neural network (CNN)-based automated segmentation method allows for reliably delineating the LC in in vivo MR images. The obtained results indicate performance superior to the inter-rater agreement, i.e. approximately 70% Dice similarity coefficient (DSC).
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