Uncertainty Evaluation Metric for Brain Tumour Segmentation
This work addresses the need for reliable uncertainty quantification in medical imaging, specifically for brain tumour segmentation, but is incremental as it focuses on evaluation rather than new methods.
The paper tackles the problem of evaluating uncertainty measures for brain tumour segmentation by developing a metric that rewards high confidence for correct assertions and penalizes under-confidence, tested on the BraTS 2019 dataset.
In this paper, we develop a metric designed to assess and rank uncertainty measures for the task of brain tumour sub-tissue segmentation in the BraTS 2019 sub-challenge on uncertainty quantification. The metric is designed to: (1) reward uncertainty measures where high confidence is assigned to correct assertions, and where incorrect assertions are assigned low confidence and (2) penalize measures that have higher percentages of under-confident correct assertions. Here, the workings of the components of the metric are explored based on a number of popular uncertainty measures evaluated on the BraTS 2019 dataset.