Convolutional neural network-based segmentation can help in assessing the substantia nigra in neuromelanin MRI.
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 t...
| Publicado en: | Neuroradiology Vol. 61; no. 12; pp. 1387 - 1396 |
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| Autores principales: | , , , , , , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=139600374&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139600374 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Dec2019 vid: 61 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 139600374 139600374 144092008 139600374 10.1007/s00234-019-02279-w 139600374 ppf: 1387 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Convolutional neural network-based segmentation can help in assessing the substantia nigra in neuromelanin MRI. aug: au: Le Berre, Alice Kamagata, Koji Otsuka, Yujiro Andica, Christina Hatano, Taku Saccenti, Laetitia Ogawa, Takashi Takeshige-Amano, Haruka Wada, Akihiko Suzuki, Michimasa Hagiwara, Akifumi Irie, Ryusuke Hori, Masaaki Oyama, Genko Shimo, Yashushi Umemura, Atsushi Hattori, Nobutaka Aoki, Shigeki affil: Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-ku, 113-8421, Tokyo, Japan sug: subj: Artificial Intelligence Methods Neural Networks (Computer) Magnetic Resonance Imaging Methods Brain Stem Analysis Parkinson Disease Diagnosis Human Scanners Radiologists Models, Statistical ROC Curve Validity ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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