CT Liver volumetry
Challenge in CT LIVER volumetry
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
* Not available for sales in all regions.
- Byass, P. The global burden of liver disease: a challenge for methods and for public health. BMC Med. 2014; 12: 159.
- Golse, N. Should We Have Blind Faith in Liver Volumetry? SurgicalCase Reports doi: 10.31487/j.SCR.2019.01.003.
- Gotra, A. Liver segmentation: indications, techniques and future directions. Insights Imaging (2017) 8:377–392.
- Favelier, S. Anatomy of liver arteries for interventional radiology. Diagnostic and Interventional Imaging (2015) 96, 537—546.
- Suzuki, K. Quantitative Radiology: Automated CT Liver Volumetry Compared With Interactive Volumetry and Manual Volumetry. AJR:197, October 2011.
- Lodewick, TM. Fast and accurate liver volumetry prior to hepatectomy. International Hepato-Pancreato-Biliary Association, HPB 2016, 18, 764–772.
- Golse. N. Should We Have Blind Faith in Liver Volumetry? SURGICAL CASE REPORTS | ISSN 2613-5965.
- Data on file (GE internal document).
- Timing performance based on Z440 hardware.
- Clinical evaluation of Hepatic VCAR, GE internal document.
- IARC database of 2018.
- JAMA Oncol. 2017;3(12):1683-1691. doi:10.1001/jamaoncol.2017.3055Published online October 5, 2017. Corrected on December 14, 2017.