Left ventricle segmentation By modelling uncertainty in prediction of deep convolutional neural networks and adaptive thresholding inference

TL;DR

This paper introduces a method to estimate uncertainty in deep neural network predictions for medical image segmentation by applying input deformations and adaptive thresholding, improving reliability and performance.

Contribution

It presents a novel approach combining input deformation-based uncertainty estimation with adaptive thresholding for improved segmentation accuracy.

Findings

Achieved state-of-the-art segmentation performance.

Demonstrated effective uncertainty estimation in neural network outputs.

Validated method on MRI cardiac images for left ventricle segmentation.

Abstract

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.