A 3D Deep Neural Network for Liver Volumetry in 3T Contrast-Enhanced MRI.
Rofo
; 193(3): 305-314, 2021 Mar.
Article
em En
| MEDLINE
| ID: mdl-32882724
ABSTRACT
PURPOSE:
To create a fully automated, reliable, and fast segmentation tool for Gd-EOB-DTPA-enhanced MRI scans using deep learning. MATERIALS ANDMETHODS:
Datasets of Gd-EOB-DTPA-enhanced liver MR images of 100 patients were assembled. Ground truth segmentation of the hepatobiliary phase images was performed manually. Automatic image segmentation was achieved with a deep convolutional neural network.RESULTS:
Our neural network achieves an intraclass correlation coefficient (ICC) of 0.987, a Sørensen-Dice coefficient of 96.7â± 1.9â% (meanâ±âstd), an overlap of 92â±â3.5â%, and a Hausdorff distance of 24.9â±â14.7âmm compared with two expert readers who corresponded to an ICC of 0.973, a Sørensen-Dice coefficient of 95.2â±â2.8â%, and an overlap of 90.9â±â4.9â%. A second human reader achieved a Sørensen-Dice coefficient of 95â% on a subset of the test set.CONCLUSION:
Our study introduces a fully automated liver volumetry scheme for Gd-EOB-DTPA-enhanced MR imaging. The neural network achieves competitive concordance with the ground truth regarding ICC, Sørensen-Dice, and overlap compared with manual segmentation. The neural network performs the task in just 60 seconds. KEY POINTS · The proposed neural network helps to segment the liver accurately, providing detailed information about patient-specific liver anatomy and volume.. · With the help of a deep learning-based neural network, fully automatic segmentation of the liver on MRI scans can be performed in seconds.. · A fully automatic segmentation scheme makes liver segmentation on MRI a valuable tool for treatment planning.. CITATION FORMAT · Winther H, Hundt C, Ringe KI etâal. A 3D Deep Neural Network for Liver Volumetry in 3T Contrast-Enhanced MRI. Fortschr Röntgenstr 2021; 193 305â-â314.
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Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
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Imageamento por Ressonância Magnética
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Redes Neurais de Computação
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Fígado
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article