# A Decoupled Uncertainty Model for MRI Segmentation Quality Estimation

arXiv: https://arxiv.org/abs/2109.02413 · HTML: https://mlsanity.com/paper/2109.02413/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 42 references that are in the map.
- First posted on arXiv 6 Sep 2021.

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty](https://mlsanity.com/paper/1912.02781/) (2019)
- [A Probabilistic U-Net for Segmentation of Ambiguous Images](https://mlsanity.com/paper/1806.05034/) (2018)
- [Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks](https://mlsanity.com/paper/1807.07356/) (2018)
- [NiftyNet: a deep-learning platform for medical imaging](https://mlsanity.com/paper/1709.03485/) (2017)
- [Leveraging Uncertainty Estimates for Predicting Segmentation Quality](https://mlsanity.com/paper/1807.00502/) (2018)
- [Supervised Uncertainty Quantification for Segmentation with Multiple Annotations](https://mlsanity.com/paper/1907.01949/) (2019)
- [Bayesian QuickNAT: Model Uncertainty in Deep Whole-Brain Segmentation for Structure-wise Quality Control](https://mlsanity.com/paper/1811.09800/) (2018)
- [Deep Generative Model-based Quality Control for Cardiac MRI Segmentation](https://mlsanity.com/paper/2006.13379/) (2020)
- [Let's agree to disagree: learning highly debatable multirater labelling](https://mlsanity.com/paper/1909.01891/) (2019)
- [MRQy: An Open-Source Tool for Quality Control of MR Imaging Data](https://mlsanity.com/paper/2004.04871/) (2020)
- [Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement](https://mlsanity.com/paper/1907.13418/) (2019)
- [Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning](https://mlsanity.com/paper/1806.06595/) (2018)
- [A Heteroscedastic Uncertainty Model for Decoupling Sources of MRI Image Quality](https://mlsanity.com/paper/2001.11927/) (2020)
- [As easy as 1, 2... 4? Uncertainty in counting tasks for medical imaging](https://mlsanity.com/paper/1907.11555/) (2019)
- [Dual Neural Network Architecture for Determining Epistemic and Aleatoric Uncertainties](https://mlsanity.com/paper/1910.06153/) (2019)
- [A deep learning-based pipeline for error detection and quality control of brain MRI segmentation results](https://mlsanity.com/paper/2005.13987/) (2020)

## Built on by (most cited first, 3 of 3)

- [Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis](https://mlsanity.com/paper/2210.03736/) (2022)
- [QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results](https://mlsanity.com/paper/2112.10074/) (2021)
- [Automated MRI Quality Assessment of Brain T1-weighted MRI in Clinical Data Warehouses: A Transfer Learning Approach Relying on Artefact Simulation](https://mlsanity.com/paper/2406.12448/) (2024)

## Abstract (arXiv)

Quality control (QC) of MR images is essential to ensure that downstream analyses such as segmentation can be performed successfully. Currently, QC is predominantly performed visually and subjectively, at significant time and operator cost. We aim to automate the process using a probabilistic network that estimates segmentation uncertainty through a heteroscedastic noise model, providing a measure of task-specific quality. By augmenting training images with k-space artefacts, we propose a novel CNN architecture to decouple sources of uncertainty related to the task and different k-space artefacts in a self-supervised manner. This enables the prediction of separate uncertainties for different types of data degradation. While the uncertainty predictions reflect the presence and severity of artefacts, the network provides more robust and generalisable segmentation predictions given the quality of the data. We show that models trained with artefact augmentation provide informative measures of uncertainty on both simulated artefacts and problematic real-world images identified by human raters, both qualitatively and quantitatively in the form of error bars on volume measurements. Relating artefact uncertainty to segmentation Dice scores, we observe that our uncertainty predictions provide a better estimate of MRI quality from the point of view of the task (gray matter segmentation) compared to commonly used metrics of quality including signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), hence providing a real-time quality metric indicative of segmentation quality.