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

Abstract

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.Index Terms-anatomical segmentation, autoencoders, convolutional neural networks, learning representations, postprocessing I. INTRODUCTIONA NATOMICAL segmentation is a fundamental task in medical image computing, which consists in associating pixels of a medical image with a given organ or anatomical structure. It constitutes an essential step in many imaging pipelines such as computer assisted diagnosis, morphometric analysis for population studies and radiotherapy planning. The correctness and anatomical plausibility of these results is thus of paramount importance, since it will directly influence the overall quality of subsequent analyses.

Learning With Context Feedback Loop for Robust Medical Image Segmentation

“…Using the feedback loop reduces the 3D HD error value by 4.3 mm over not using it (only FS). Attention gated-based method [38] has only reduced the 3D HD metric over the U-net [13] by 0.9 mm, while the Post-DAE method [34] reduced it by 6.2 mm. However, the Post-DAE did not improve the average Dice index.…”

“…It needs the modeling of the prior knowledge, for example, anatomical shapes, and then embed it into the U-Net architectures to constrain the learning process [6] [25] [27]. Other approaches include post-processing methods based on either denoising [34] or variational auto-encoders [35] and showed an improvement in the plausibility of the results. However, they are not free of limitations.…”

Deep Neural Architectures for Medical Image Semantic Segmentation: Review

“…Similar to IoU, it also ranges from 0 to 1. Value 1 depicts the highest similarity between the predicted value and the ground truth [130]. Hence, it finds VOLUME 4, 2016 the similarity between the two data samples.…”

“…This metric focuses on objects and calculates the topological error amongst them rather than focusing on pixel variance. It is essentially the least mean square error between the pixels of the objective segmentation and the pixels of a topologypreserving distorted source, segmentation [130]. Mathematically, it is expressed in ( 13) using euclidean distance, or equivalently, hamming distance.…”

Medical Image Segmentation With Limited Supervision: A Review of Deep Network Models

“…Shape-prior based segmentation [94]- [96], [107]- [113] has been an active research topic in the context of deep learning to obtain more accurate and anatomically plausible segmentation. While principal component analysis (PCA) based statistical shape model (SSM) [30] was widely adopted by traditional segmentation methods, it is not straightforward to combine SSM with deep networks.…”