Fully automated deep learning-based localization and segmentation of the locus coeruleus in aging and Parkinson's disease using neuromelanin-sensitive MRI.

Journal: International journal of computer assisted radiology and surgery

PMID: 34797512

Abstract

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.

Authors

Max Dünnwald

Department of Neurology, Faculty of Medicine, Otto von Guericke University Magdeburg (OVGU), Magdeburg, Germany. max.duennwald@med.ovgu.de.
Philipp Ernst
Emrah Düzel

German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.
Klaus Tönnies
Matthew J Betts

German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.
Steffen Oeltze-Jafra

Innovation Center Computer Assisted Surgery, Faculty of Medicine, University Leipzig, Leipzig, Germany.

Keywords

Deep Learning Humans Image Processing, Computer-Assisted Locus Coeruleus Magnetic Resonance Imaging Melanins Parkinson Disease

External Resources

View on PubMed Access via DOI PubMed (34797512)

Fully automated deep learning-based localization and segmentation of the locus coeruleus in aging and Parkinson's disease using neuromelanin-sensitive MRI.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals