CT Liver volumetry

Challenge in CT LIVER volumetry

hepatic vcar*

Automatic CT Liver segmentation
based on deep learning

  • Overall success rate for automatic liver segmentation on the testing set
  • AVERAGE TIME to achieve liver  segmentation


  • Inter reader variability on the liver volume  measurement when edits were needed

Complete reading workflow solution featuring:

    • Intelligent liver lesion segmentation using auto contour tool.
    • Deep learning algorithm for liver segmentation at different phases and hepatic artery segmentation
    • Intuitive tools to segment the liver into its segments or lobes
    • Tumor burden calculation
    • Efficient & consistent reporting tools to facilitate communication

Supporting Materials

* Not available for sales in all regions.

  1. Byass, P. The global burden of liver disease: a challenge for methods and for public health. BMC Med. 2014; 12: 159.
  2. Golse, N. Should We Have Blind Faith in Liver Volumetry? SurgicalCase Reports doi: 10.31487/j.SCR.2019.01.003.
  3. Gotra, A. Liver segmentation: indications, techniques and future directions. Insights Imaging (2017) 8:377–392.
  4. Favelier, S. Anatomy of liver arteries for interventional radiology. Diagnostic and Interventional Imaging (2015) 96, 537—546.
  5. Suzuki, K. Quantitative Radiology: Automated CT Liver Volumetry Compared With Interactive Volumetry and Manual Volumetry. AJR:197, October 2011.
  6. Lodewick, TM. Fast and accurate  liver volumetry prior to hepatectomy. International Hepato-Pancreato-Biliary Association, HPB 2016, 18, 764–772.
  7. Golse. N. Should We Have Blind Faith in Liver Volumetry? SURGICAL CASE REPORTS | ISSN 2613-5965.
  8. Data on file (GE internal document).
  9. Timing performance based on Z440 hardware.
  10. Clinical evaluation of Hepatic VCAR, GE internal document.
  11. IARC database of 2018.
  12. JAMA Oncol. 2017;3(12):1683-1691. doi:10.1001/jamaoncol.2017.3055Published online October 5, 2017. Corrected on December 14, 2017.