Convolutional neural network-based segmentation can help in assessing the substantia nigra in neuromelanin MRI.
Neuroradiology
; 61(12): 1387-1395, 2019 Dec.
Article
em En
| MEDLINE
| ID: mdl-31401723
ABSTRACT
PURPOSE:
This study aimed to evaluate the accuracy and diagnostic test performance of the U-net-based segmentation method in neuromelanin magnetic resonance imaging (NM-MRI) compared to the established manual segmentation method for Parkinson's disease (PD) diagnosis.METHODS:
NM-MRI datasets from two different 3T-scanners were used a "principal dataset" with 122 participants and an "external validation dataset" with 24 participants, including 62 and 12 PD patients, respectively. Two radiologists performed SNpc manual segmentation. Inter-reader precision was determined using Dice coefficients. The U-net was trained with manual segmentation as ground truth and Dice coefficients used to measure accuracy. Training and validation steps were performed on the principal dataset using a 4-fold cross-validation method. We tested the U-net on the external validation dataset. SNpc hyperintense areas were estimated from U-net and manual segmentation masks, replicating a previously validated thresholding method, and their diagnostic test performances for PD determined.RESULTS:
For SNpc segmentation, U-net accuracy was comparable to inter-reader precision in the principal dataset (Dice coefficient U-net, 0.83 ± 0.04; inter-reader, 0.83 ± 0.04), but lower in external validation dataset (Dice coefficient U-net, 079 ± 0.04; inter-reader, 0.85 ± 0.03). Diagnostic test performances for PD were comparable between U-net and manual segmentation methods in both principal (area under the receiver operating characteristic curve U-net, 0.950; manual, 0.948) and external (U-net, 0.944; manual, 0.931) datasets.CONCLUSION:
U-net segmentation provided relatively high accuracy in the evaluation of the SNpc in NM-MRI and yielded diagnostic performance comparable to that of the established manual method.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Doença de Parkinson
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Substância Negra
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Imageamento por Ressonância Magnética
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Interpretação de Imagem Assistida por Computador
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Melaninas
Tipo de estudo:
Guideline
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Observational_studies
/
Prognostic_studies
Limite:
Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article