TABLE 1.

Strengths and limitations of algorithm categories

Algorithm Category	Strengths	Limitations
Low Level Image Processing (e.g., Thresholding and Edge Detection)	Simple implementation low computational complexity	Sensitive to the image’s noise and artifacts Perform poorly when applied to obscured images and images with unclear boundaries, non-uniform regional intensities, and confusing structures
Deformable Models (e.g., Active Contour Model)	Can segment any shape Highly flexible	Sensitive to the initial contour location/shape Perform poorly when the shape vary widely Tend to become computationally complex
Statistical Models (e.g., Active Appearance Model)	Use intensity and shape information Highly effective	Require proper initialization Expensive manual shapes annotations Perform poorly when the shape vary widely Local minimum trap
Conventional Machine Learning (e.g., Random Forest Trees)	Good to high performance Good interpretability	Look at specific handcrafted features Bias of engineer who designs the method Require a set of annotated data
Deep Learning (e.g., Convolutional Neural Network)	Superior performance	Require a large set of annotated data Long tuning/training process Lack of interpretability