Table 7.

Advantages and disadvantages of segmentation methods.

Algorithms	Advantages	Disadvantages
Watershed [225]	Being able to divide an image into its components	Takes too long to run in order to meet the deadline, sensitivity to false edges and over-segmentation
U-Net [226]	Images can be segmented quickly and accurately	Redundancy occurs due to patch overlap, also relatively slow
MV-CNN [203]	No user-interactive parameters or assumptions about the shape of nodules are needed	The loss of gradients may have an effect
CF-CNN [206]	Gathered sensitive information about nodules from CT imaging data	Less adaptable for small nodules and cavitary nodules
FCM [188]	Ignored noise sensitivity limitation, successfully overcoming the PCM's clustering problem	Row sum constraints must be equal to one in order to work
Hessian-based approaches [209]	High robustness against noise and sensitivity to small objects	Performance decreases for large nodule
SegNet + shape driven level set [213]	Correct seed point initialization with no manual intervention in the level set	Segments the lung nodule partly occluded, also takes a longer time
Faster R-CNN [214]	The efficiency of detection is high	It could take a long time to reach convergence
Mask R-CNN [218]	Easy to train, generalizable to other tasks, effective, and only adds a minor overhead	Low-resolution motion blur detection typically fails to pick up on objects
RASM [219]	Well suited to large shape models and parallel implementation allowing for short computation times	Cannot segment areas with sharp angles and is not built to handle juxta-pleural nodules
Region growing [227]	The concept is simple, multiple criteria can be selected simultaneously, and it performs well in terms of noise	Computing is time-consuming. Noise or variation may result in holes or over-segmentation, making it difficult to distinguish the shading of real images.