  We propose a 2D Encoder-Decoder based deep learning architecture for semantic
segmentation, that incorporates anatomical priors by imitating the encoder
component of an autoencoder in latent space. The autoencoder is additionally
enhanced by means of hierarchical features, extracted by an U-Net module. Our
suggested architecture is trained in an end-to-end manner and is evaluated on
the example of pelvic bone segmentation in MRI. A comparison to the standard
U-Net architecture shows promising improvements.
