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We introduce Post-DAE, a post-processing method based on denoisingautoencoders (DAE) to improve the anatomical plausibility of arbitrarybiomedical image segmentation algorithms. Some of the most popular segmentationmethods (e.g. based on convolutional neural networks or random forestclassifiers) incorporate additional post-processing steps to ensure that theresulting masks fulfill expected connectivity constraints. These methodsoperate under the hypothesis that contiguous pixels with similar aspect shouldbelong to the same class. Even if valid in general, this assumption does notconsider more complex priors like topological restrictions or convexity, whichcannot be easily incorporated into these methods. Post-DAE leverages the latestdevelopments in manifold learning via denoising autoencoders. First, we learn acompact and non-linear embedding that represents the space of anatomicallyplausible segmentations. Then, given a segmentation mask obtained with anarbitrary method, we reconstruct its anatomically plausible version byprojecting it onto the learnt manifold. The proposed method is trained usingunpaired segmentation mask, what makes it independent of intensity informationand image modality. We performed experiments in binary and multi-labelsegmentation of chest X-ray and cardiac magnetic resonance images. We show howerroneous and noisy segmentation masks can be improved using Post-DAE. Withalmost no additional computation cost, our method brings erroneoussegmentations back to a feasible space.

Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising Autoencoders