Related Experiment Video
Updated: Jan 18, 2026
Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease
Published on: March 24, 2023
Evaluation of artificial-intelligence-based liver segmentation and its application for longitudinal liver volume
Rina Kimura1,2, Kenji Hirata3,4,5,6, Satonori Tsuneta2,7
1Department of Diagnostic Imaging, Faculty of Medicine, Hokkaido University, Sapporo, Japan.
Background:
Accurate liver-volume measurements from CT scans are essential for treatment planning, particularly in liver resection cases, to avoid postoperative liver failure. However, manual segmentation is time-consuming and prone to variability. Advancements in artificial intelligence (AI), specifically convolutional neural networks, have enhanced liver segmentation accuracy. We aimed to identify optimal CT phases for AI-based liver volume estimation and apply the model to track liver volume changes over time. We also evaluated temporal changes in liver volume in participants without liver disease.
Methods:
In this retrospective, single-center study, we assessed the performance of an open-source AI-based liver segmentation model previously reported, using non-contrast and dynamic CT phases. The accuracy of the model was compared with that of expert radiologists. The Dice similarity coefficient (DSC) was calculated across various CT phases, including arterial, portal venous, and non-contrast, to validate the model. The model was then applied to a longitudinal study involving 39 patients without liver disease (527 CT scans) to examine age-related liver volume changes over 5 to 20 years.
Results:
The model demonstrated high accuracy across all phases compared to manual segmentation. Among the CT phases, the highest DSC of 0.988 ± 0.010 was in the arterial phase. The intraclass correlation coefficients for liver volume were also high, exceeding 0.9 for contrast-enhanced phases and 0.8 for non-contrast CT. In the longitudinal study, the model indicated an annual decrease of 0.95%.
Conclusion:
This model provides high accuracy in liver segmentation across various CT phases and offers insights into age-related liver volume reduction. Measuring changes in liver volume may help with the early detection of diseases and the understanding of pathophysiology.
