Application of a U-Net for map-like segmentation and classification of discontinuous fibrosis distribution in Gd-EOB-DTPA-enhanced liver MRI

Abstract: We aimed to evaluate whether U-shaped convolutional neuronal networks can be used to segment liver parenchyma and indicate the degree of liver fibrosis/cirrhosis at the voxel level using contrast-enhanced magnetic resonance imaging. This retrospective study included 112 examinations with histologically determined liver fibrosis/cirrhosis grade (Ishak score) as the ground truth. The T1-weighted volume-interpolated breath-hold examination sequences of native, arterial, late arterial, portal venous, and hepatobiliary phases were semi-automatically segmented and co-registered. The segmentations were assigned the corresponding Ishak score. In a nested cross-validation procedure, five models of a convolutional neural network with U-Net architecture (nnU-Net) were trained, with the dataset being divided into stratified training/validation (n = 89/90) and holdout test datasets (n = 23/22). The trained models precisely segmented the test data (mean dice similarity coefficient = 0.938) and assigned separate fibrosis scores to each voxel, allowing localization-dependent determination of the degree of fibrosis. The per voxel results were evaluated by the histologically determined fibrosis score. The micro-average area under the receiver operating characteristic curve of this seven-class classification problem (Ishak score 0 to 6) was 0.752 for the test data. The top-three-accuracy-score was 0.750. We conclude that determining fibrosis grade or cirrhosis based on multiphase Gd-EOB-DTPA-enhanced liver MRI seems feasible using a 2D U-Net. Prospective studies with localized biopsies are needed to evaluate the reliability of this model in a clinical setting

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• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch
• Anmerkungen: Diagnostics. - 12, 8 (2022) , 1938, ISSN: 2075-4418

Beteiligte, Orts- und Zeitangaben

• Ereignis: Veröffentlichung
• (wo): Freiburg
• (wer): Universität
• (wann): 2022
• Urheber: Strotzer, Quirin David ; Winther, Hinrich Boy Martin ; Utpatel, Kirsten ; Scheiter, Alexander ; Fellner, Claudia Maria ; Doppler, Michael Christian ; Ringe, Kristina Imeen ; Raab, Florian ; Haimerl, Michael ; Uller, Wibke ; Stroszczynski, Christian ; Luerken, Lukas ; Verloh, Niklas

Weitere Informationen

• DOI: 10.3390/diagnostics12081938
• URN: urn:nbn:de:bsz:25-freidok-2299815
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.