We introduce Post-DAE, a post-processing method based on denoising
autoencoders (DAE) to improve the anatomical plausibility of arbitrary
biomedical image segmentation algorithms. Some of the most popular segmentation
methods (e.g. based on convolutional neural networks or random forest
classifiers) incorporate additional post-processing steps to ensure that the
resulting masks fulfill expected connectivity constraints. These methods
operate under the hypothesis that contiguous pixels with similar aspect should
belong to the same class. Even if valid in general, this assumption does not
consider more complex priors like topological restrictions or convexity, which
cannot be easily incorporated into these methods. Post-DAE leverages the latest
developments in manifold learning via denoising autoencoders. First, we learn a
compact and non-linear embedding that represents the space of anatomically
plausible segmentations. Then, given a segmentation mask obtained with an
arbitrary method, we reconstruct its anatomically plausible version by
projecting it onto the learnt manifold. The proposed method is trained using
unpaired segmentation mask, what makes it independent of intensity information
and image modality. We performed experiments in binary and multi-label
segmentation of chest X-ray and cardiac magnetic resonance images. We show how
erroneous and noisy segmentation masks can be improved using Post-DAE. With
almost no additional computation cost, our method brings erroneous
segmentations back to a feasible space.