TABLE 1.

Current ANTsRNet Capabilities Comprising Architectures for Applications in Image Segmentation, Image Classification, Object Localization, and Image Super-Resolution. Self-Contained Examples with Data are also Provided to Demonstrate Usage for Each of the Architectures. Although the Majority of Neural Network Architectures are Originally Described for 2-D Images, we Generalized the Work to 3-D Implementations Where Possible

		ANTsRNet
Image Segmentation
U-net [23]	(2-D)	Extends fully convolutional neural networks by including an upsampling decoding path with skip connections linking corresponding encoding/decoding layers.
V-net [47]	(3-D)	3-D extension of U-net which incorporates a customized Dice loss function.
Image Classification
AlexNet [16]	(2-D, 3-D)	Convolutional neural network that precipitated renewed interest in neural networks.
VGG16/VGG19 [17]	(2-D, 3-D)	Also known as “OxfordNet.” VGG architectures are much deeper than AlexNet. Two popular styles are implemented.
GoogLeNet [18]	(2-D)	A 22-layer network formed from inception blocks meant to reduce the number of parameters relative to other architectures.
ResNet [48]	(2-D, 3-D)	Characterized by specialized residualized blocks (and skip connections.
ResNeXt [49]	(2-D, 3-D)	A variant of ResNet distinguished by a hyperparameter called cardinality defining the number of independent paths.
DenseNet [50]	(2-D, 3-D)	Based on the observation that performance is typically enhanced with shorter connections between the layers and the input.
Object Localization
SSD [51]	(2-D, 3-D)	The Multibox Single-Shot Detection (SSD) algorithm for determining bounding boxes around objects of interest.
SSD7 [52]	(2-D, 3-D)	Lightweight SSD variant which increases speed by slightly sacrificing accuracy. Training size requirements are smaller.
Image super-resolution
SRCNN [53]	(2-D, 3-D)	Image super-resolution using CNNs.