  Probabilistic atlases (PAs) have long been used in standard segmentation
approaches and, more recently, in conjunction with Convolutional Neural
Networks (CNNs). However, their use has been restricted to relatively
standardized structures such as the brain or heart which have limited or
predictable range of deformations. Here we propose an encoding-decoding CNN
architecture that can exploit rough atlases that encode only the topology of
the target structures that can appear in any pose and have arbitrarily complex
shapes to improve the segmentation results. It relies on the output of the
encoder to compute both the pose parameters used to deform the atlas and the
segmentation mask itself, which makes it effective and end-to-end trainable.
