ArticleJournal of healthcare engineering2018
Fully Automatic Segmentation and Three-Dimensional Reconstruction of the Liver in CT Images.
Article in Journal of healthcare engineering, 2018. 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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5 citing papers in PubMed, 13 citations in OpenAlex.
- Effect of laparoscopic anatomical hepatectomy for hepatocellular carcinoma based on individualized three-dimensional reconstruction model of portal vein region.World journal of surgical oncology · 2026Article
- Deep learning-based system to predict hepatocellular carcinoma resection volume using contrast-enhanced CT.Scientific reports · 2026Article
- Development and validation of a simplified CT volumetry for estimating total liver volume in patients with autosomal dominant polycystic kidney and liver disease.Clinical and experimental nephrology · 2025Article
- Simple diameter measurement as predictor of liver volume and liver parenchymal disease.Scientific reports · 2022Article
- Liver segmentation in CT imaging with enhanced mask region-based convolutional neural networks.Annals of translational medicine · 2021Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automatic segmentation and three-dimensional reconstruction of the liver is important for liver disease diagnosis and surgical treatment. However, the shape of the imaged 2D liver in each CT image changes dramatically across the slices. In all slices, the imaged 2D liver is connected with other organs, and the connected organs also vary across the slices. In many slices, the intensities of the connected organs are the same with that of the liver. All these facts make automatic segmentation of the liver in the CT image an extremely difficult task. In this paper, we propose a heuristic approach to segment the liver automatically based on multiple thresholds. The thresholds are computed based on the slope difference distribution that has been proposed and verified in the previous research. Different organs in the CT image are segmented with the automatically computed thresholds, respectively. Then, different segmentation results are combined to delineate the boundary of the liver robustly. After the boundaries of the 2D liver in all the slices are identified, they are combined to form the 3D shape of the liver with a global energy minimization function. Experimental results verified the effectiveness of all the proposed image processing algorithms in automatic and robust segmentation of the liver in CT images.
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