from functools import partial
from keras.layers import Input, Add, UpSampling2D, Activation, SpatialDropout2D, Conv2D, Permute, LeakyReLU
from keras.engine import Model
from keras.optimizers import Adam
from .unet import create_convolution_block, concatenate
from ...metrics import weighted_dice_coefficient_loss, dice_coefficient_loss, vod_coefficient_loss, dice_coefficient, \
vod_coefficient
create_convolution_block = partial(create_convolution_block, activation=LeakyReLU, instance_normalization=True)
def isensee2017_model(input_shape=(4, 128, 128, 128), n_base_filters=16, depth=5, dropout_rate=0.3,
n_segmentation_levels=3, n_labels=1, optimizer=Adam, initial_learning_rate=5e-4,
loss_function=dice_coefficient_loss, activation_name="sigmoid", summation=False, **kargs):
"""
This function builds a model proposed by Isensee et al. for the BRATS 2017 competition:
https://www.cbica.upenn.edu/sbia/Spyridon.Bakas/MICCAI_BraTS/MICCAI_BraTS_2017_proceedings_shortPapers.pdf
This network is highly similar to the model proposed by Kayalibay et al. "CNN-based Segmentation of Medical
Imaging Data", 2017: https://arxiv.org/pdf/1701.03056.pdf
:param input_shape:
:param n_base_filters:
:param depth:
:param dropout_rate:
:param n_segmentation_levels:
:param n_labels:
:param optimizer:
:param initial_learning_rate:
:param loss_function:
:param activation_name:
:return:
"""
metrics = ['binary_accuracy', vod_coefficient]
if loss_function != dice_coefficient_loss:
metrics += [dice_coefficient]
inputs = Input(input_shape)
inputs_p = Permute((3, 1, 2))(inputs)
current_layer = inputs_p
level_output_layers = list()
level_filters = list()
for level_number in range(depth):
n_level_filters = (2 ** level_number) * n_base_filters
level_filters.append(n_level_filters)
if current_layer is inputs_p:
in_conv = create_convolution_block(current_layer, n_level_filters)
else:
in_conv = create_convolution_block(current_layer, n_level_filters, strides=(2, 2))
context_output_layer = create_context_module(in_conv, n_level_filters, dropout_rate=dropout_rate)
summation_layer = Add()([in_conv, context_output_layer])
level_output_layers.append(summation_layer)
current_layer = summation_layer
segmentation_layers = list()
for level_number in range(depth - 2, -1, -1):
up_sampling = create_up_sampling_module(current_layer, level_filters[level_number])
concatenation_layer = concatenate([level_output_layers[level_number], up_sampling], axis=1)
localization_output = create_localization_module(concatenation_layer, level_filters[level_number])
current_layer = localization_output
if level_number < n_segmentation_levels:
segmentation_layers.insert(0, Conv2D(n_labels, (1, 1))(current_layer))
if summation:
output_layer = None
for level_number in reversed(range(n_segmentation_levels)):
segmentation_layer = segmentation_layers[level_number]
if output_layer is None:
output_layer = segmentation_layer
else:
output_layer = Add()([output_layer, segmentation_layer])
if level_number > 0:
output_layer = UpSampling2D(size=(2, 2))(output_layer)
else:
output_layer = segmentation_layers[0]
activation_block = Activation(activation_name)(output_layer)
activation_block = Permute((2, 3, 1))(activation_block)
model = Model(inputs=inputs, outputs=activation_block)
model.compile(optimizer=optimizer(lr=initial_learning_rate), loss=loss_function,
metrics=metrics)
return model
def create_localization_module(input_layer, n_filters):
convolution1 = create_convolution_block(input_layer, n_filters)
convolution2 = create_convolution_block(convolution1, n_filters, kernel=(1, 1))
return convolution2
def create_up_sampling_module(input_layer, n_filters, size=(2, 2)):
up_sample = UpSampling2D(size=size)(input_layer)
convolution = create_convolution_block(up_sample, n_filters)
return convolution
def create_context_module(input_layer, n_level_filters, dropout_rate=0.3, data_format="channels_first"):
convolution1 = create_convolution_block(input_layer=input_layer, n_filters=n_level_filters)
dropout = SpatialDropout2D(rate=dropout_rate, data_format=data_format)(convolution1)
convolution2 = create_convolution_block(input_layer=dropout, n_filters=n_level_filters)
return convolution2
