Convolutional neural network for automated segmentation of the liver and its vessels on non-contrast T1 vibe Dixon acquisitions

Zbinden, Lukas; Catucci, Damiano; Suter, Yannick; Berzigotti, Annalisa; Ebner, Lukas; Christe, Andreas; Obmann, Verena Carola; Sznitman, Raphael; Huber, Adrian Thomas (2022). Convolutional neural network for automated segmentation of the liver and its vessels on non-contrast T1 vibe Dixon acquisitions. Scientific Reports, 12(1) Nature Publishing Group 10.1038/s41598-022-26328-2

[img]
Preview
Text
s41598-022-26328-2.pdf - Published Version
Available under License Creative Commons: Attribution (CC-BY).

Download (5MB) | Preview

We evaluated the effectiveness of automated segmentation of the liver and its vessels with a convolutional neural network on non-contrast T1 vibe Dixon acquisitions. A dataset of non-contrast T1 vibe Dixon liver magnetic resonance images was labelled slice-by-slice for the outer liver border, portal, and hepatic veins by an expert. A 3D U-Net convolutional neural network was trained with different combinations of Dixon in-phase, opposed-phase, water, and fat reconstructions. The neural network trained with the single-modal in-phase reconstructions achieved a high performance for liver parenchyma (Dice 0.936 ± 0.02), portal veins (0.634 ± 0.09), and hepatic veins (0.532 ± 0.12) segmentation. No benefit of using multi -modal input was observed (p=1.0 for all experiments), combining in-phase, opposed-phase, fat, and water reconstruction. Accuracy for differentiation between portal and hepatic veins was 99% for portal veins and 97% for hepatic veins in the central region and slightly lower in the peripheral region (91% for portal veins, 80% for hepatic veins). In conclusion, deep learning-based automated segmentation of the liver and its vessels on non-contrast T1 vibe Dixon was highly effective. The single-modal in-phase input achieved the best performance in segmentation and differentiation between portal and hepatic veins.

Item Type:

Journal Article (Original Article)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Medical Imaging Laboratory
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology
04 Faculty of Medicine > Department of Gastro-intestinal, Liver and Lung Disorders (DMLL) > Clinic of Visceral Surgery and Medicine > Hepatology
04 Faculty of Medicine > Department of Gastro-intestinal, Liver and Lung Disorders (DMLL) > Clinic of Visceral Surgery and Medicine
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Zbinden, Lukas, Catucci, Damiano Livio Aldo, Suter, Yannick Raphael, Berzigotti, Annalisa, Ebner, Lukas, Christe, Andreas, Obmann, Verena Carola, Sznitman, Raphael, Huber, Adrian Thomas

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health
000 Computer science, knowledge & systems

ISSN:

2045-2322

Publisher:

Nature Publishing Group

Language:

English

Submitter:

Lukas Zbinden

Date Deposited:

21 Dec 2022 12:42

Last Modified:

23 May 2024 14:02

Publisher DOI:

10.1038/s41598-022-26328-2

BORIS DOI:

10.48350/175839

URI:

https://boris.unibe.ch/id/eprint/175839

Actions (login required)

Edit item Edit item
Provide Feedback