Left ventricle segmentation By modelling uncertainty in prediction of deep convolutional neural networks and adaptive thresholding inference
This addresses uncertainty estimation for medical imaging segmentation, which is incremental as it builds on existing deep learning methods with a novel uncertainty modeling approach.
The paper tackled the problem of estimating uncertainty in deep neural network outputs by introducing input deformations and measuring output stability, applied to left ventricle segmentation in MRI cardiac images, achieving state-of-the-art performance.
Deep neural networks have shown great achievements in solving complex problems. However, there are fundamental problems that limit their real world applications. Lack of measurable criteria for estimating uncertainty in the network outputs is one of these problems. In this paper, we address this limitation by introducing deformation to the network input and measuring the level of stability in the network's output. We calculate simple random transformations to estimate the prediction uncertainty of deep convolutional neural networks. For a real use-case, we apply this method to left ventricle segmentation in MRI cardiac images. We also propose an adaptive thresholding method to consider the deep neural network uncertainty. Experimental results demonstrate state-of-the-art performance and highlight the capabilities of simple methods in conjunction with deep neural networks.