Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks.

ABSTRACT: Despite the state-of-the-art performance for medical image segmentation, deep convolutional neural networks (CNNs) have rarely provided uncertainty estimations regarding their segmentation outputs, e.g., model (epistemic) and image-based (aleatoric) uncertainties. In this work, we analyze these different types of uncertainties for CNN-based 2D and 3D medical image segmentation tasks at both pixel level and structure level. We additionally propose a test-time augmentation-based aleatoric uncertainty to analyze the effect of different transformations of the input image on the segmentation output. Test-time augmentation has been previously used to improve segmentation accuracy, yet not been formulated in a consistent mathematical framework. Hence, we also propose a theore

SUBMITTER: Wang G 

PROVIDER: S-EPMC6783308 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature