Table 2.

Quantitative comparison of rule and deformable model-based lung segmentation methods. Accuracy, dice similarity coefficient (DSC), jaccard index (JI), and inference time are reported for comparison

Methods	Dataset	Technique	Results(%)			Time(s)
			Accuracy	DSC	JI
Intensity based [9]	Private	Thresholding, Filtering, Contour smoothing	79.0	–	–	–
Edge based [40]	Private	Edge tracing algorithm	95.85	–	–	0.84
Edge based [94]	Private	Derivative function, Iterative contour smoothing	95.6	–	–	0.775
Knowledge based [102]	Private	Robust snake model, Textual, Spatial and Shape characteristics	80.0	–	–	–
Parametric model [167]	Private	ASM, Adaptive gray-level appearance model, kNN	–	–	95.5	4.1
Parametric model [149]	JSRT	SIFT, Optimization algorithm	–	–	94.9	75
Geometric model [6]	Private	Level set energy, Equalized histogram, Canny edge detector, Otsu thresholding	–	88.0	–	7
Geometric model [17]	JSRT	Lung model calculation, Graph cut segmentation	91.0	91.0	–	8
Geometric model [19]	JSRT	Adaptive parameter learning, Graph cut segmentation	–	–	91.1	8
Geometric model [18]	JSRT, MC	Image retrieval, SIFT flow, Graph cut optimization	95.4	–	–	20–25
Hybrid model [148]	JSRT	Robust shape initialization, Local sparse shape composition, Local appearance model, Hierarchical deformable segmentation framework	–	97.2	94.6	35.2
Hybrid method [181]	JSRT	Modified gradient vector flow-ASM	–	–	89.1	–