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Table 2 common semiautomated segmentation algorithms used in HCC

From: The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges

Segmentation

Algorithm

Description

Performance

Setback

Image intensity based [8, 32, 33]

Region growing e.g. GrowCut

Uses region-growing seed points to segment a tumor

Fast, low computational complexity, good reproducibility strong correlation with macroscopic tumor diameter

Segmentation errors due to boundary leakages, unsuitable for highly heterogenous tumors

GraphCut

Constructs an image-graph of voxels connected by weighted edges

Can deal with tumors with odd shapes and mosaic intensity

Over segmentation or undesired ROIs when there are artefacts

Water shed transformation

Segments tumor from parenchyma based on difference in gray scale intensity

Global segmentation

Over segmentation sensitive to poor tumor margins

Contour-based approach [34, 35]

Active contours, level-set and Live wires

Iteratively marks tumor contour from starting points on tumor edge

Faster than region growing methods

Rely on good initialization points and speed functions, sensitive to noise and poor tumor margins