niftynet.network.dense_vnet module¶
-
class
niftynet.network.dense_vnet.
Affine3DAugmentationLayer
(scale, interpolation, boundary, transform_func=None, name='AffineAugmentation')¶ Bases:
niftynet.layer.base_layer.TrainableLayer
-
inverse
(interpolation=None, boundary=None)¶
-
inverse_transform
(batch_size)¶
-
layer_op
(input_tensor)¶
-
random_transform
(batch_size)¶
-
-
class
niftynet.network.dense_vnet.
DenseFeatureStackBlock
(n_dense_channels, kernel_size, dilation_rates, use_bdo, name='dense_feature_stack_block', **kwargs)¶ Bases:
niftynet.layer.base_layer.TrainableLayer
-
layer_op
(input_tensor, is_training=None, keep_prob=None)¶
-
-
class
niftynet.network.dense_vnet.
DenseFeatureStackBlockWithSkipAndDownsample
(n_dense_channels, kernel_size, dilation_rates, n_seg_channels, n_downsample_channels, use_bdo, name='dense_feature_stack_block', **kwargs)¶ Bases:
niftynet.layer.base_layer.TrainableLayer
-
layer_op
(input_tensor, is_training=None, keep_prob=None)¶
-
-
class
niftynet.network.dense_vnet.
DenseVNet
(num_classes, hyperparameters={}, architecture_parameters={}, w_initializer=None, w_regularizer=None, b_initializer=None, b_regularizer=None, acti_func='relu', name='DenseVNet')¶ Bases:
niftynet.network.base_net.BaseNet
- implementation of Dense-V-Net:
- Gibson et al., “Automatic multi-organ segmentation on abdominal CT with dense V-networks”
-
layer_op
(input_tensor, is_training, layer_id=-1)¶
-
class
niftynet.network.dense_vnet.
SpatialPriorBlock
(prior_shape, output_shape, name='spatial_prior_block')¶ Bases:
niftynet.layer.base_layer.TrainableLayer
-
layer_op
()¶
-
-
niftynet.network.dense_vnet.
image_resize
(image, output_size)¶