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)
