Analyzing the Quality and Challenges of Uncertainty Estimations for Brain Tumor Segmentation.

ABSTRACT: Automatic segmentation of brain tumors has the potential to enable volumetric measures and high-throughput analysis in the clinical setting. Reaching this potential seems almost achieved, considering the steady increase in segmentation accuracy. However, despite segmentation accuracy, the current methods still do not meet the robustness levels required for patient-centered clinical use. In this regard, uncertainty estimates are a promising direction to improve the robustness of automated segmentation systems. Different uncertainty estimation methods have been proposed, but little is known about their usefulness and limitations for brain tumor segmentation. In this study, we present an analysis of the most commonly used uncertainty estimation methods in regards to benefits and challenges fo

SUBMITTER: Jungo A 

PROVIDER: S-EPMC7156850 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature