Hierarchical Uncertainty Estimation for Medical Image Segmentation Networks

• URL: http://arxiv.org/abs/2308.08465v1
• Date: Wed, 16 Aug 2023 16:09:23 GMT
• Title: Hierarchical Uncertainty Estimation for Medical Image Segmentation Networks
• Authors: Xinyu Bai, Wenjia Bai
• Abstract summary: Uncertainty exists in both images (noise) and manual annotations (human errors and bias) used for model training. We propose a simple yet effective method for estimating uncertainties at multiple levels. We demonstrate that a deep learning segmentation network such as U-net, can achieve a high segmentation performance.
• Score: 1.9564356751775307
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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