Automated liver segmental volume ratio quantification on non-contrast T1-Vibe Dixon liver MRI using deep learning.

Zbinden, Lukas; Catucci, Damiano; Suter, Yannick Raphael; Hulbert, Leona; Berzigotti, Annalisa; Brönnimann, Michael; Ebner, Lukas; Christe, Andreas; Obmann, Verena Carola; Sznitman, Raphael; Huber, Adrian Thomas (2023). Automated liver segmental volume ratio quantification on non-contrast T1-Vibe Dixon liver MRI using deep learning. European journal of radiology, 167, p. 111047. Elsevier 10.1016/j.ejrad.2023.111047

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PURPOSE

To evaluate the effectiveness of automated liver segmental volume quantification and calculation of the liver segmental volume ratio (LSVR) on a non-contrast T1-vibe Dixon liver MRI sequence using a deep learning segmentation pipeline.

METHOD

A dataset of 200 liver MRI with a non-contrast 3 mm T1-vibe Dixon sequence was manually labeledslice-by-sliceby an expert for Couinaud liver segments, while portal and hepatic veins were labeled separately. A convolutional neural networkwas trainedusing 170 liver MRI for training and 30 for evaluation. Liver segmental volumes without liver vessels were retrieved and LSVR was calculated as the liver segmental volumes I-III divided by the liver segmental volumes IV-VIII. LSVR was compared with the expert manual LSVR calculation and the LSVR calculated on CT scans in 30 patients with CT and MRI within 6 months.

RESULTS

Theconvolutional neural networkclassified the Couinaud segments I-VIII with an average Dice score of 0.770 ± 0.03, ranging between 0.726 ± 0.13 (segment IVb) and 0.810 ± 0.09 (segment V). The calculated mean LSVR with liver MRI unseen by the model was 0.32 ± 0.14, as compared with manually quantified LSVR of 0.33 ± 0.15, resulting in a mean absolute error (MAE) of 0.02. A comparable LSVR of 0.35 ± 0.14 with a MAE of 0.04 resulted with the LSRV retrieved from the CT scans. The automated LSVR showed significant correlation with the manual MRI LSVR (Spearman r = 0.97, p < 0.001) and CT LSVR (Spearman r = 0.95, p < 0.001).

CONCLUSIONS

A convolutional neural network allowed for accurate automated liver segmental volume quantification and calculation of LSVR based on a non-contrast T1-vibe Dixon sequence.

Item Type:

Journal Article (Original Article)

Division/Institute:

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

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

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

Subjects:

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

ISSN:

1872-7727

Publisher:

Elsevier

Language:

English

Submitter:

Pubmed Import

Date Deposited:

12 Sep 2023 09:43

Last Modified:

20 Aug 2024 16:37

Publisher DOI:

10.1016/j.ejrad.2023.111047

PubMed ID:

37690351

Uncontrolled Keywords:

Artificial Intelligence Biomarker Cirrhosis Liver Magnetic Resonance Imaging

BORIS DOI:

10.48350/186215

URI:

https://boris.unibe.ch/id/eprint/186215

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