Winther, Hinrich and Hundt, Christian and Ringe, Kristina Imeen and Wacker, Frank K. and Schmidt, Bertil and Juergens, Julian and Haimerl, Michael and Beyer, Lukas Philipp and Stroszczynski, Christian and Wiggermann, Philipp and Verloh, Niklas (2021) A 3D Deep Neural Network for Liver Volumetry in 3T Contrast-Enhanced MRI. ROFO-FORTSCHRITTE AUF DEM GEBIET DER RONTGENSTRAHLEN UND DER BILDGEBENDEN VERFAHREN, 193 (03). pp. 305-314. ISSN 1438-9029, 1438-9010
Full text not available from this repository. (Request a copy)Abstract
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 performedmanually. 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 +/- 14.7mmcompared 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | AUTOMATIC LIVER; HEPATIC-UPTAKE; HEPATOBILIARY PHASE; ACTIVE CONTOURS; SEGMENTATION; DTPA; CT; REMNANT; AGENT; GADOXETATE; liver segmentation; liver volumetry; semantic segmentation; fully automated segmentation; contrast-enhanced liver MRI |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Röntgendiagnostik |
| Depositing User: | Petra Gürster |
| Date Deposited: | 16 Apr 2021 08:23 |
| Last Modified: | 16 Apr 2021 08:23 |
| URI: | https://pred.uni-regensburg.de/id/eprint/43854 |
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