In:
International Journal of Computer Assisted Radiology and Surgery, Springer Science and Business Media LLC, Vol. 16, No. 12 ( 2021-12), p. 2129-2135
Kurzfassung:
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.
Materialart:
Online-Ressource
ISSN:
1861-6410
,
1861-6429
DOI:
10.1007/s11548-021-02528-5
Sprache:
Englisch
Verlag:
Springer Science and Business Media LLC
Publikationsdatum:
2021
ZDB Id:
2235881-X
