Evaluation of artificial-intelligence-based liver segmentation and its application for longitudinal liver volume measurement.
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), speci...
| Publicado en: | Abdominal Radiology Vol. 50; no. 12; pp. 6192 - 6201 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189212018&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189212018 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Dec2025 vid: 50 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 189212018 185830951 10.1007/s00261-025-05050-3 189212018 ppf: 6192 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Evaluation of artificial-intelligence-based liver segmentation and its application for longitudinal liver volume measurement. aug: au: Kimura, Rina Hirata, Kenji Tsuneta, Satonori Takenaka, Junki Watanabe, Shiro Abo, Daisuke Kudo, Kohsuke affil: https://ror.org/02e16g702 Department of Diagnostic Imaging, Faculty of Medicine, Hokkaido University, Sapporo, Japan sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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