# Estimating Uncertainty in Neural Networks for Cardiac MRI Segmentation: A Benchmark Study

arXiv: https://arxiv.org/abs/2012.15772 · HTML: https://mlsanity.com/paper/2012.15772/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2020.

## Built on by (most cited first, 19 of 19)

- [A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods](https://mlsanity.com/paper/2310.06873/) (2023)
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- [A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation](https://mlsanity.com/paper/2411.16370/) (2024)
- [Benchmarking Scalable Epistemic Uncertainty Quantification in Organ Segmentation](https://mlsanity.com/paper/2308.07506/) (2023)
- [Confidence Contours: Uncertainty-Aware Annotation for Medical Semantic Segmentation](https://mlsanity.com/paper/2308.07528/) (2023)
- [Influence of uncertainty estimation techniques on false-positive reduction in liver lesion detection](https://mlsanity.com/paper/2206.10911/) (2022)
- [Uncertainty estimates for semantic segmentation: providing enhanced reliability for automated motor claims handling](https://mlsanity.com/paper/2401.09245/) (2024)
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- [Improving Uncertainty-Error Correspondence in Deep Bayesian Medical Image Segmentation](https://mlsanity.com/paper/2409.03470/) (2024)
- [Uncertainty Propagation for Echocardiography Clinical Metric Estimation via Contour Sampling](https://mlsanity.com/paper/2502.12713/) (2025)
- [Uncertainty evaluation of segmentation models for Earth observation](https://mlsanity.com/paper/2510.19586/) (2025)
- [Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations](https://mlsanity.com/paper/2307.06424/) (2023)
- [Temporal Uncertainty Localization to Enable Human-in-the-loop Analysis of Dynamic Contrast-enhanced Cardiac MRI Datasets](https://mlsanity.com/paper/2308.13488/) (2023)
- [Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data Adaptive Uncertainty-guided Space-time Analysis](https://mlsanity.com/paper/2408.04805/) (2024)
- [QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Prediction](https://mlsanity.com/paper/2412.07156/) (2024)

## Abstract (arXiv)

Objective: Convolutional neural networks (CNNs) have demonstrated promise in automated cardiac magnetic resonance image segmentation. However, when using CNNs in a large real-world dataset, it is important to quantify segmentation uncertainty and identify segmentations which could be problematic. In this work, we performed a systematic study of Bayesian and non-Bayesian methods for estimating uncertainty in segmentation neural networks. Methods: We evaluated Bayes by Backprop, Monte Carlo Dropout, Deep Ensembles, and Stochastic Segmentation Networks in terms of segmentation accuracy, probability calibration, uncertainty on out-of-distribution images, and segmentation quality control. Results: We observed that Deep Ensembles outperformed the other methods except for images with heavy noise and blurring distortions. We showed that Bayes by Backprop is more robust to noise distortions while Stochastic Segmentation Networks are more resistant to blurring distortions. For segmentation quality control, we showed that segmentation uncertainty is correlated with segmentation accuracy for all the methods. With the incorporation of uncertainty estimates, we were able to reduce the percentage of poor segmentation to 5% by flagging 31--48% of the most uncertain segmentations for manual review, substantially lower than random review without using neural network uncertainty (reviewing 75--78% of all images). Conclusion: This work provides a comprehensive evaluation of uncertainty estimation methods and showed that Deep Ensembles outperformed other methods in most cases. Significance: Neural network uncertainty measures can help identify potentially inaccurate segmentations and alert users for manual review.