Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation

Li, H; Nan, Y; Del Ser, J; Yang, G

AI Collection

Journal article

Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation

Abstract:: Despite recent advances in the accuracy of brain tumor segmentation, the results still suffer from low reliability and robustness. Uncertainty estimation is an efficient solution to this problem, as it provides a measure of confidence in the segmentation results. The current uncertainty estimation methods based on quantile regression, Bayesian neural network, ensemble, and Monte Carlo dropout are limited by their high computational cost and inconsistency. In order to overcome these challenges, Evidential Deep Learning (EDL) was developed in recent work but primarily for natural image classification and showed inferior segmentation results. In this paper, we proposed a region-based EDL segmentation framework that can generate reliable uncertainty maps and accurate segmentation results, which is robust to noise and image corruption. We used the Theory of Evidence to interpret the output of a neural network as evidence values gathered from input features. Following Subjective Logic, evidence was parameterized as a Dirichlet distribution, and predicted probabilities were treated as subjective opinions. To evaluate the performance of our model on segmentation and uncertainty estimation, we conducted quantitative and qualitative experiments on the BraTS 2020 dataset. The results demonstrated the top performance of the proposed method in quantifying segmentation uncertainty and robustly segmenting tumors. Furthermore, our proposed new framework maintained the advantages of low computational cost and easy implementation and showed the potential for clinical application.

Publication status:: Published

Peer review status:: Peer reviewed

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APA Style

Li, H., Nan, Y., Del Ser, J., & Yang, G. (2022). Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation. Neural Computing and Applications, 35(30), 22071–22085.

MLA Style

Li, H, et al. “Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation.” Neural Computing and Applications, vol. 35, no. 30, 2022, pp. 22071–85.

Chicago Style

Li, H, Y Nan, J Del Ser, and G Yang. 2022. “Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation.” Neural Computing and Applications 35 (30): 22071–85.
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