ArticleInternational journal of computer assisted radiology and surgery2017
Automated liver segmentation from a postmortem CT scan based on a statistical shape model.
Article in International journal of computer assisted radiology and surgery, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 14 citations in OpenAlex.
- A hybrid approach based on deep learning and level set formulation for liver segmentation in CT images.Journal of applied clinical medical physics · 2022Article
- An automated liver segmentation in liver iron concentration map using fuzzy c-means clustering combined with anatomical landmark data.BMC medical imaging · 2021Article
- Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images.Korean journal of radiology · 2020Article
- Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks.International journal of computer assisted radiology and surgery · 2019Article
- Fully Automatic Segmentation and Three-Dimensional Reconstruction of the Liver in CT Images.Journal of healthcare engineering · 2018Article
- Liver segmentation from CT images using a sparse priori statistical shape model (SP-SSM).PloS one · 2017Article
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Authors and funding
4 authors at 2 institutions in 1 country.
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Abstract
purposeAutomated liver segmentation from a postmortem computed tomography (PMCT) volume is a challenging problem owing to the large deformation and intensity changes caused by severe pathology and/or postmortem changes. This paper addresses this problem by a novel segmentation algorithm using a statistical shape model (SSM) for a postmortem liver.
methodsThe location and shape parameters of a liver are directly estimated from a given volume by the proposed SSM-guided expectation-maximization (EM) algorithm without any spatial standardization that might fail owing to the large deformation and intensity changes. The estimated location and shape parameters are then used as a constraint of the subsequent fine segmentation process based on graph cuts. Algorithms with eight different SSMs were trained using 144 in vivo and 32 postmortem livers, and the segmentation algorithm was tested on 32 postmortem livers in a twofold cross validation manner. The segmentation performance is measured by the Jaccard index (JI) between the segmentation result and the true liver label.
resultsThe average JI of the segmentation result with the best SSM was 0.8501, which was better compared with the results obtained using conventional SSMs and the results of the previous postmortem liver segmentation with statistically significant difference.
conclusionsWe proposed an algorithm for automated liver segmentation from a PMCT volume, in which an SSM-guided EM algorithm estimated the location and shape parameters of a liver in a given volume accurately. We demonstrated the effectiveness of the proposed algorithm using actual postmortem CT volumes.
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