ArticlePloS one2021
Fully automated whole-liver volume quantification on CT-image data: Comparison with manual volumetry using enhanced and unenhanced images as well as two different radiation dose levels and two reconstruction kernels.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Comparing the reliability of three software programmes for manual and (semi-)automatic volumetry in healthy livers.World journal of radiology · 2026Article
- Medium- and Long-Term Evaluation of Splenic Arterial Embolization: A Retrospective CT Volumetric and Hematologic Function Analysis.Journal of personalized medicine · 2025Article
- Intra-Individual Reproducibility of Automated Abdominal Organ Segmentation-Performance of TotalSegmentator Compared to Human Readers and an Independent nnU-Net Model.Journal of imaging informatics in medicine · 2025Article
- Fully-automated multi-organ segmentation tool applicable to both non-contrast and post-contrast abdominal CT: deep learning algorithm developed using dual-energy CT images.Scientific reports · 2024Article
- Liver volumetric and anatomic assessment in living donor liver transplantation: The role of modern imaging and artificial intelligence.World journal of transplantation · 2023Review
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Abstract
objectivesTo evaluate the accuracy of fully automated liver volume quantification vs. manual quantification using unenhanced as well as enhanced CT-image data as well as two different radiation dose levels and also two image reconstruction kernels. MATERIAL AND
methodsThe local ethics board gave its approval for retrospective data analysis. Automated liver volume quantification in 300 consecutive livers in 164 male and 103 female oncologic patients (64±12y) performed at our institution (between January 2020 and May 2020) using two different dual-energy helicals: portal-venous phase enhanced, ref. tube current 300mAs (CARE Dose4D) for tube A (100 kV) and ref. 232mAs tube current for tube B (Sn140kV), slice collimation 0.6mm, reconstruction kernel I30f/1, recon. thickness of 0.6mm and 5mm, 80-100 mL iodine contrast agent 350 mg/mL, (flow 2mL/s) and unenhanced ref. tube current 100mAs (CARE Dose4D) for tube A (100 kV) and ref. 77mAs tube current for tube B (Sn140kV), slice collimation 0.6mm (kernel Q40f) were analyzed. The post-processing tool (syngo.CT Liver Analysis) is already FDA-approved. Two resident radiologists with no and 1-year CT-experience performed both the automated measurements independently from each other. Results were compared with those of manual liver volume quantification using the same software which was supervised by a senior radiologist with 30-year CT-experience (ground truth).
resultsIn total, a correlation of 98% was obtained for liver volumetry based on enhanced and unenhanced data sets compared to the manual liver quantification. Radiologist #1 and #2 achieved an inter-reader agreement of 99.8% for manual liver segmentation (p<0.0001). Automated liver volumetry resulted in an overestimation (>5% deviation) of 3.7% for unenhanced CT-image data and 4.0% for contrast-enhanced CT-images. Underestimation (<5%) of liver volume was 2.0% for unenhanced CT-image data and 1.3% for enhanced images after automated liver volumetry. Number and distribution of erroneous volume measurements using either thin or thick slice reconstructions was exactly the same, both for the enhanced as well for the unenhanced image data sets (p> 0.05).
conclusionResults of fully automated liver volume quantification are accurate and comparable with those of manual liver volume quantification and the technique seems to be confident even if unenhanced lower-dose CT image data is used.
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