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Automatic substantia nigra segmentation in neuromelanin-sensitive MRI by deep neural network in patients with prodromal and manifest synucleinopathy
R. Krupička, S. Mareček, C. Malá, M. Lang, O. Klempíř, T. Duspivová, R. Široká, T. Jarošíková, J. Keller, K. Šonka, E. Růžička, P. Dušek
Language English Country Czech Republic
Document type Clinical Trial, Journal Article
Grant support
NV15-25602A
MZ0
CEP Register
Digital library NLK
Full text - Article
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- MeSH
- Deep Learning * MeSH
- Middle Aged MeSH
- Humans MeSH
- Magnetic Resonance Imaging methods MeSH
- Melanins analysis MeSH
- Image Processing, Computer-Assisted MeSH
- Prodromal Symptoms MeSH
- Aged MeSH
- Case-Control Studies MeSH
- Substantia Nigra diagnostic imaging MeSH
- Synucleinopathies diagnostic imaging MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Clinical Trial MeSH
Neuromelanin (NM) is a black pigment located in the brain in substantia nigra pars compacta (SN) and locus coeruleus. Its loss is directly connected to the loss of nerve cells in this part of the brain, which plays a role in Parkinson's Disease. Magnetic resonance imaging (MRI) is an ideal tool to monitor the amount of NM in the brain in vivo. The aim of the study was the development of tools and methodology for the quantification of NM in a special neuromelanin-sensitive MRI images. The first approach was done by creating regions of interest, corresponding to the anatomical position of SN based on an anatomical atlas and determining signal intensity threshold. By linking the anatomical and signal intensity information, we were able to segment the SN. As a second approach, the neural network U-Net was used for the segmentation of SN. Subsequently, the volume characterizing the amount of NM in the SN region was calculated. To verify the method and the assumptions, data available from various patient groups were correlated. The main benefit of this approach is the observer-independency of quantification and facilitation of the image processing process and subsequent quantification compared to the manual approach. It is ideal for automatic processing many image sets in one batch.
Czech Technical University Prague Faculty of Biomedical Engineering Kladno Czech Republic
Department of Radiodiagnostics Na Homolce Hospital Prague Czech Republic
References provided by Crossref.org
Literatura
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- $a Neuromelanin (NM) is a black pigment located in the brain in substantia nigra pars compacta (SN) and locus coeruleus. Its loss is directly connected to the loss of nerve cells in this part of the brain, which plays a role in Parkinson's Disease. Magnetic resonance imaging (MRI) is an ideal tool to monitor the amount of NM in the brain in vivo. The aim of the study was the development of tools and methodology for the quantification of NM in a special neuromelanin-sensitive MRI images. The first approach was done by creating regions of interest, corresponding to the anatomical position of SN based on an anatomical atlas and determining signal intensity threshold. By linking the anatomical and signal intensity information, we were able to segment the SN. As a second approach, the neural network U-Net was used for the segmentation of SN. Subsequently, the volume characterizing the amount of NM in the SN region was calculated. To verify the method and the assumptions, data available from various patient groups were correlated. The main benefit of this approach is the observer-independency of quantification and facilitation of the image processing process and subsequent quantification compared to the manual approach. It is ideal for automatic processing many image sets in one batch.
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