ArticleScientific reports2023
Automated 3D liver segmentation from hepatobiliary phase MRI for enhanced preoperative planning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Effect of virtual reality on spatial-anatomical understanding in preoperative liver surgery: a randomized crossover study.Scientific reports · 2026Trial
- Automated Whole-Liver Fat Quantification with Magnetic Resonance Imaging-Derived Proton Density Fat Fraction Map: A Prospective Study in Taiwan.Gut and liver · 2025Trial
- Prediction model for hypertrophied liver volume followed by hemihepatectomy: a retrospective cohort study.Annals of surgical treatment and research · 2026Article
- Predicting post-hepatectomy liver failure after hemi-hepatectomy using a combined model based on age, ALBI score, and the future liver remnant-to-spleen volume ratio.Updates in surgery · 2026Article
- Intraoperative video-based artificial intelligence model exceeding surgeon accuracy for predicting severe fibrosis in minimally invasive liver surgery.Scientific reports · 2026Article
- Three-dimensional reconstruction of lung tumors from computed tomography scans using adversarial and transductive learning.Scientific reports · 2025Article
- MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach.European radiology experimental · 2025Article
- Automatic segmentation of liver structures in multi-phase MRI using variants of nnU-Net and Swin UNETR.Scientific reports · 2025Article
- Updates, Applications and Future Directions of Deep Learning for the Images Processing in the Field of Cranio-Maxillo-Facial Surgery.Bioengineering (Basel, Switzerland) · 2025Review
- MHAU-Net: a multi-scale hybrid attention U-shaped network for the segmentation of MRI breast tumors.Quantitative imaging in medicine and surgery · 2025Article
- Systematic Review: AI Applications in Liver Imaging with a Focus on Segmentation and Detection.Life (Basel, Switzerland) · 2025Review
- Adaptive Evolutionary Optimization of Deep Learning Architectures for Focused Liver Ultrasound Image Segmentation.Diagnostics (Basel, Switzerland) · 2025Article
- Predictors of Post-Hepatectomy Liver Failure in Klatskin Tumors: The Role of Preoperative Glucose, Future Liver Remnant to Spleen Ratio, and Early Bilirubin Monitoring.Diagnostics (Basel, Switzerland) · 2024Article
- A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models.Diagnostics (Basel, Switzerland) · 2024Article
- Vascular liver segmentation: a narrative review on methods and new insights brought by artificial intelligence.The Journal of international medical research · 2024Review
- Multivariable linear model for predicting graft weight based on 3-dimensional volumetry in regards to body weight change of living liver donor: an observational cohort study.Annals of surgical treatment and research · 2024Article
- Fully automated assessment of the future liver remnant in a blood-free setting via CT before major hepatectomy via deep learning.Insights into imaging · 2024Article
- Signal Intensity Trajectories Clustering for Liver Vasculature Segmentation and Labeling (LiVaS) on Contrast-Enhanced MR Images: A Feasibility Pilot Study.Journal of imaging informatics in medicine · 2024Article
- Predicting Safe Liver Resection Volume for Major Hepatectomy Using Artificial Intelligence.Journal of clinical medicine · 2024Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advancements in deep learning have facilitated significant progress in medical image analysis. However, there is lack of studies specifically addressing the needs of surgeons in terms of practicality and precision for surgical planning. Accurate understanding of anatomical structures, such as the liver and its intrahepatic structures, is crucial for preoperative planning from a surgeon's standpoint. This study proposes a deep learning model for automatic segmentation of liver parenchyma, vascular and biliary structures, and tumor mass in hepatobiliary phase liver MRI to improve preoperative planning and enhance patient outcomes. A total of 120 adult patients who underwent liver resection due to hepatic mass and had preoperative gadoxetic acid-enhanced MRI were included in the study. A 3D residual U-Net model was developed for automatic segmentation of liver parenchyma, tumor mass, hepatic vein (HV), portal vein (PV), and bile duct (BD). The model's performance was assessed using Dice similarity coefficient (DSC) by comparing the results with manually delineated structures. The model achieved high accuracy in segmenting liver parenchyma (DSC 0.92 ± 0.03), tumor mass (DSC 0.77 ± 0.21), hepatic vein (DSC 0.70 ± 0.05), portal vein (DSC 0.61 ± 0.03), and bile duct (DSC 0.58 ± 0.15). The study demonstrated the potential of the 3D residual U-Net model to provide a comprehensive understanding of liver anatomy and tumors for preoperative planning, potentially leading to improved surgical outcomes and increased patient safety.
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