Liver Segmentation
Liver Segmentation - Related Videos
04:41
Laparoscopic Left Liver Sectoriectomy of Caroli's Disease Limited to Segment II and III
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Cited by 5 •
2009
This video presents a case of laparoscopic left liver sectoriectomy perfomed in a 53 year old man suffering from Caroli's disease limited to segment II and III of the liver.
13:57
Laparoscopic Anatomical Liver Segment VII Resection with Liver Parenchymal Transection Following a Priority Approach
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2025
This protocol involves a laparoscopic anatomical resection of liver segment VII with liver parenchymal transection following a priority approach. This technique is unaffected by anatomical variations.
12:13
Ex Situ Normothermic Machine Perfusion of Donor Livers
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Cited by 21 •
2015
Here we present a protocol describing oxygenated ex situ machine perfusion of donor liver grafts. This article contains a step by step protocol to procure and prepare the liver graft for machine perfusion, prepare the perfusion fluid, prime the perfusion machine and perform oxygenated normothermic machine perfusion of the liver graft.
12:35
In vivo Liver Endocytosis Followed by Purification of Liver Cells by Liver Perfusion
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Cited by 14 •
2011
The study of liver sinusoidal endothelial cells (SECs) must be performed with primary cells obtained from the animal as no cell lines exist. This method relies on liver digestion and differential centrifugation for SEC purification for subsequent culturing and experimentation.
09:24
Decellularization and Recellularization of Whole Livers
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Cited by 39 •
2011
Perfusion decellularization is a novel technique to produce whole liver scaffolds that retains the organ's extracellular matrix composition and microarchitecture. Herein, the method of preparing whole organ scaffolds using perfusion decellularization and subsequent repopulation with hepatocytes is described. Functional and transplantable liver grafts can be generated using this technique.
FAQs
Boundary detection relies on combining intensity differences with anatomical features that distinguish the liver from surrounding tissues. This slice-by-slice approach creates a continuous representation rather than relying on a single image. The resulting region can then serve as a consistent basis for measurements and other analyses performed on the liver.
Trained deep-learning models can identify liver boundaries from CT or MRI data and produce masks for analysis. Their role is not necessarily final: clinicians or other reviewers can inspect the generated result and refine it when the boundary is inaccurate. This workflow combines automated image analysis with human oversight before the mask supports clinical or quantitative work.
Accurate boundaries determine which parts of the image are included in the liver region, so segmentation quality affects measurements derived from that region. Reliable masks support liver-volume measurement, lesion analysis, and standardized computer-assisted diagnosis. They also make comparisons more meaningful during longitudinal monitoring because the analyzed region is defined consistently across assessments.
After image data are processed, the generated mask is examined against the liver boundaries visible across the image slices. Inaccurate regions can be refined so the delineated area better follows the anatomical features and intensity differences in the CT or MRI study. This review produces a more dependable input for volume measurement, lesion analysis, or treatment planning.
By isolating the liver as a defined region, the method enables quantitative analysis rather than relying only on visual interpretation. Liver volume can be measured from the segmented region, while the same boundary framework supports lesion analysis. These outputs connect the anatomy shown in CT or MRI with numerical information that can contribute to clinical assessment.
Liver Segmentation is relevant when clinicians need a defined representation of liver anatomy for treatment planning or for assessing surgical and transplant options. The measured or analyzed region can also support computer-assisted diagnosis and longitudinal monitoring. In these settings, consistent boundaries help connect image findings with clinical decisions and changes observed across repeated assessments.