Preliminary study of substantia nigra analysis by tensorial feature extraction.
Computer-aided diagnosis
Feature selection
Neuromelanin imaging
Parkinson disease
Substantia nigra
Tensor decomposition
Journal
International journal of computer assisted radiology and surgery
ISSN: 1861-6429
Titre abrégé: Int J Comput Assist Radiol Surg
Pays: Germany
ID NLM: 101499225
Informations de publication
Date de publication:
27 Jun 2024
27 Jun 2024
Historique:
received:
05
04
2023
accepted:
03
05
2024
medline:
27
6
2024
pubmed:
27
6
2024
entrez:
27
6
2024
Statut:
aheadofprint
Résumé
Parkinson disease (PD) is a common progressive neurodegenerative disorder in our ageing society. Early-stage PD biomarkers are desired for timely clinical intervention and understanding of pathophysiology. Since one of the characteristics of PD is the progressive loss of dopaminergic neurons in the substantia nigra pars compacta, we propose a feature extraction method for analysing the differences in the substantia nigra between PD and non-PD patients. We propose a feature-extraction method for volumetric images based on a rank-1 tensor decomposition. Furthermore, we apply a feature selection method that excludes common features between PD and non-PD. We collect neuromelanin images of 263 patients: 124 PD and 139 non-PD patients and divide them into training and testing datasets for experiments. We then experimentally evaluate the classification accuracy of the substantia nigra between PD and non-PD patients using the proposed feature extraction method and linear discriminant analysis. The proposed method achieves a sensitivity of 0.72 and a specificity of 0.64 for our testing dataset of 66 non-PD and 42 PD patients. Furthermore, we visualise the important patterns in the substantia nigra by a linear combination of rank-1 tensors with selected features. The visualised patterns include the ventrolateral tier, where the severe loss of neurons can be observed in PD. We develop a new feature-extraction method for the analysis of the substantia nigra towards PD diagnosis. In the experiments, even though the classification accuracy with the proposed feature extraction method and linear discriminant analysis is lower than that of expert physicians, the results suggest the potential of tensorial feature extraction.
Identifiants
pubmed: 38935246
doi: 10.1007/s11548-024-03175-2
pii: 10.1007/s11548-024-03175-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Japan Society for the Promotion of Science
ID : 23K16900
Organisme : Japan Society for the Promotion of Science
ID : 21K19898
Organisme : Japan Agency for Medical Research and Development
ID : 22dm0307101h0004
Informations de copyright
© 2024. CARS.
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