Zusammenfassung
Purpose To create a fully automated, reliable, and fast segmentation tool for Gd-EOB-DTPA-enhanced MRI scans using deep learning. Materials and Methods 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 ...
Zusammenfassung
Purpose To create a fully automated, reliable, and fast segmentation tool for Gd-EOB-DTPA-enhanced MRI scans using deep learning. Materials and Methods 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 Sorensen-Dice coefficient of 96.7 +/- 1.9 % (mean +/- std), an overlap of 92 +/- 3.5 %, and a Hausdorff distance of 24.9 +/- 14.7 mm compared with two expert readers who corresponded to an ICC of 0.973, a Sorensen-Dice coefficient of 95.2 +/- 2.8 %, and an overlap of 90.9 +/- 4.9 %. A second human reader achieved a Sorensen-Dice coefficient of 95 % 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, Sorensen-Dice, and overlap compared with manual segmentation. The neural network performs the task in just 60 seconds.
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