オオヤマ　ゲンコウ   OYAMA Genko   大山　彦光    所属   埼玉医科大学  医学部 脳神経内科    職種   教授
論文種別	学術雑誌(原著)
言語種別	英語
査読の有無	査読あり
表題	Convolutional neural network-based segmentation can help in assessing the substantia nigra in neuromelanin MRI.
掲載誌名	正式名：Neuroradiology
掲載区分	国外
巻・号・頁	61(12),1387-1395頁
著者・共著者	Alice Le Berre,Koji Kamagata,Yujiro Otsuka,Christina Andica,Taku Hatano,Laetitia Saccenti,Takashi Ogawa,Haruka Takeshige-Amano,Akihiko Wada,Michimasa Suzuki,Akifumi Hagiwara,Ryusuke Irie,Masaaki Hori,Genko Oyama,Yashushi Shimo,Atsushi Umemura,Nobutaka Hattori,Shigeki Aoki
発行年月	2019/12
概要	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.
DOI	10.1007/s00234-019-02279-w
PMID	31401723