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Neuroradiology. 2019 Aug 10. doi: 10.1007/s00234-019-02279-w. [Epub ahead of print]

Convolutional neural network-based segmentation can help in assessing the substantia nigra in neuromelanin MRI.

Author information

1
Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
2
Department of Radiology, Université Paris Descartes, 12 rue de l'Ecole de Medecine, 75006, Paris, France.
3
Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan. kkamagat@juntendo.ac.jp.
4
Milliman Inc., Tokyo, Japan.
5
Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan.
6
Department of Neurosurgery, Juntendo University School of Medicine, Tokyo, Japan.

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.

KEYWORDS:

Artificial intelligence; Magnetic resonance imaging; Neural networks (computer); Parkinson disease

PMID:
31401723
DOI:
10.1007/s00234-019-02279-w

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