def build(conf, name='Enc_Anatomy'):
"""
Build a UNet based encoder to extract anatomical information from the image.
"""
spatial_encoder = UNet(conf)
spatial_encoder.input = Input(shape=conf.input_shape)
l1_down = spatial_encoder.unet_downsample(
spatial_encoder.input, spatial_encoder.normalise) # downsample
spatial_encoder.unet_bottleneck(l1_down,
spatial_encoder.normalise) # bottleneck
l2_up = spatial_encoder.unet_upsample(
spatial_encoder.bottleneck, spatial_encoder.normalise) # upsample
anatomy = Conv2D(conf.out_channels,
1,
padding='same',
activation='softmax',
name='conv_anatomy')(l2_up)
if conf.rounding:
anatomy = Rounding()(anatomy)
model = Model(inputs=spatial_encoder.input, outputs=anatomy, name=name)
log.info('Enc_Anatomy')
model.summary(print_fn=log.info)
return model
def _decomposer(self):
"""
Build an image decomposer into a spatial binary mask of the myocardium and a non-spatial vector z of the
remaining image information.
:return a Keras model of the decomposer
"""
input = Input(self.conf.input_shape)
unet = UNet(self.conf.input_shape, residual=False)
l = unet.unet_downsample(input)
unet.unet_bottleneck(l)
l = unet.bottleneck
# build Z regressor
modality = Conv2D(256, 3, strides=1, padding='same')(l)
modality = BatchNormalization()(modality)
modality = LeakyReLU()(modality)
modality = Conv2D(64, 3, strides=1, padding='same')(modality)
modality = BatchNormalization()(modality)
modality = LeakyReLU()(modality)
modality = Flatten()(modality)
modality = Dense(32)(modality)
modality = LeakyReLU()(modality)
modality = Dense(16, activation='sigmoid')(modality)
l = unet.unet_upsample(unet.bottleneck)
anatomy = unet.out(l)
m = Model(inputs=input, outputs=[anatomy, modality], name='Decomposer')
log.info('Decomposer')
m.summary(print_fn=log.info)
return m
def build_anatomy_encoder(self):
"""
Build an encoder to extract anatomical information from the image.
"""
# Manually build UNet to add Rounding as a last layer
spatial_encoder = UNet(self.conf.anatomy_encoder_params)
spatial_encoder.input = Input(shape=self.conf.input_shape)
l1 = spatial_encoder.unet_downsample(spatial_encoder.input,
spatial_encoder.normalise)
spatial_encoder.unet_bottleneck(l1, spatial_encoder.normalise)
l2 = spatial_encoder.unet_upsample(spatial_encoder.bottleneck,
spatial_encoder.normalise)
anatomy = spatial_encoder.out(l2, out_activ='softmax')
if self.conf.rounding == 'encoder':
anatomy = Rounding()(anatomy)
self.Enc_Anatomy = Model(inputs=spatial_encoder.input,
outputs=anatomy,
name='Enc_Anatomy')
log.info('Enc_Anatomy')
self.Enc_Anatomy.summary(print_fn=log.info)
