Zbinden, LukasLukasZbinden0000-0003-0681-6476Catucci, Damiano Livio AldoDamiano Livio AldoCatucci0000-0001-6053-3181Suter, Yannick RaphaelYannick RaphaelSuterHulbert, LeonaLeonaHulbertBerzigotti, AnnalisaAnnalisaBerzigotti0000-0003-4562-9016Brönnimann, MichaelMichaelBrönnimannEbner, LukasLukasEbnerChriste, AndreasAndreasChristeObmann, Verena CarolaVerena CarolaObmannSznitman, RaphaelRaphaelSznitman0000-0001-6791-4753Huber, Adrian ThomasAdrian ThomasHuber2024-10-252024-10-252023-10https://boris-portal.unibe.ch/handle/20.500.12422/169901PURPOSE 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.enArtificial Intelligence Biomarker Cirrhosis Liver Magnetic Resonance Imaging000 - Computer science, knowledge & systems500 - Science::570 - Life sciences; biology600 - Technology::610 - Medicine & healtharticle10.48350/1862153769035110.1016/j.ejrad.2023.111047