Table 2.

common semiautomated segmentation algorithms used in HCC

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