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 build(self, conf):
# build encoder1
encoder1 = UNet(conf)
encoder1.input = Input(shape=conf.input_shape)
l1 = encoder1.unet_downsample(encoder1.input, encoder1.normalise)
# build encoder2
encoder2 = UNet(conf)
encoder2.input = Input(shape=conf.input_shape)
l2 = encoder2.unet_downsample(encoder2.input, encoder2.normalise)
self.build_decoder(conf)
d1_l3 = encoder1.d_l3 if conf.downsample > 3 else None
d2_l3 = encoder2.d_l3 if conf.downsample > 3 else None
anatomy_output1 = self.evaluate_decoder(conf, l1, d1_l3, encoder1.d_l2,
encoder1.d_l1, encoder1.d_l0)
anatomy_output2 = self.evaluate_decoder(conf, l2, d2_l3, encoder2.d_l2,
encoder2.d_l1, encoder2.d_l0)
# build shared layer
shr_lay4 = Conv2D(conf.out_channels,
1,
padding='same',
activation='softmax',
name='conv_anatomy')
# connect models
encoder1_output = shr_lay4(anatomy_output1)
encoder2_output = shr_lay4(anatomy_output2)
if conf.rounding:
encoder1_output = Rounding()(encoder1_output)
encoder2_output = Rounding()(encoder2_output)
encoder1 = Model(inputs=encoder1.input,
outputs=encoder1_output,
name='Enc_Anatomy_%s' % self.modalities[0])
encoder2 = Model(inputs=encoder2.input,
outputs=encoder2_output,
name='Enc_Anatomy_%s' % self.modalities[1])
return [encoder1, encoder2]
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)
