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The deep convolutional neural network (ConvNet) achieves significant segmentation performance on medical images of various modalities. However, the isolated errors in a large testing set with various tumor conditions are not acceptable in clinical practice. This is usually caused in inadequate training and noise inherent during data collection, which are recognized as epistemic and aleatoric uncertainties in deep learning-based approaches. In this paper, we analyze the two types of uncertainties in medical image segmentation tasks and propose a reduction method by training models with data augmentation. The shelter zones in images are reduced with 2D imaging on surfaces of different angles from 3D organs. Rotation transformation and noise are estimated by Monte Carlo simulation with prior parameter distributions, and the aleatoric uncertainty is quantized in this process. Experiments on segmentation of computed tomography images demonstrate that overconfident incorrect predictions are reduced through uncertainty reduction and that our method outperforms prediction baselines based on epistemic and aleatoric estimation.

Epistemic and aleatoric uncertainties reduction with rotation variation for medical image segmentation with ConvNets