Hierarchical Uncertainty Estimation for Medical Image Segmentation
  Networks
Xinyu Bai Wenjia Bai
Learning a medical image segmentation model is an inherently ambiguous task,
as uncertainties exist in both images (noise) and manual annotations (human
errors and bias) used for model training. To build a trustworthy image
segmentation model, it is important to not just evaluate its performance but
also estimate the uncertainty of the model prediction. Most state-of-the-art
image segmentation networks adopt a hierarchical encoder architecture,
extracting image features at multiple resolution levels from fine to coarse. In
this work, we leverage this hierarchical image representation and propose a
simple yet effective method for estimating uncertainties at multiple levels.
The multi-level uncertainties are modelled via the skip-connection module and
then sampled to generate an uncertainty map for the predicted image
segmentation. We demonstrate that a deep learning segmentation network such as
U-net, when implemented with such hierarchical uncertainty estimation module,
can achieve a high segmentation performance, while at the same time provide
meaningful uncertainty maps that can be used for out-of-distribution detection.