# Automatic segmentation with detection of local segmentation failures in cardiac MRI

arXiv: https://arxiv.org/abs/2011.07025 · HTML: https://mlsanity.com/paper/2011.07025/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2020.

## Built on by (most cited first, 10 of 10)

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- [Towards Fully Automated Segmentation of Rat Cardiac MRI by Leveraging Deep Learning Frameworks](https://mlsanity.com/paper/2109.04188/) (2021)
- [Deep Learning for Automatic Strain Quantification in Arrhythmogenic Right Ventricular Cardiomyopathy](https://mlsanity.com/paper/2311.14448/) (2023)
- [Improving Uncertainty-Error Correspondence in Deep Bayesian Medical Image Segmentation](https://mlsanity.com/paper/2409.03470/) (2024)
- [Temporal Uncertainty Localization to Enable Human-in-the-loop Analysis of Dynamic Contrast-enhanced Cardiac MRI Datasets](https://mlsanity.com/paper/2308.13488/) (2023)
- [RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models](https://mlsanity.com/paper/2401.08847/) (2024)
- [QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Prediction](https://mlsanity.com/paper/2412.07156/) (2024)
- [Fighting MRI Anisotropy: Learning Multiple Cardiac Shapes From a Single Implicit Neural Representation](https://mlsanity.com/paper/2602.11436/) (2026)
- [TRUST: Threshold-Recalibrated Uncertainty-Safe Training for Certified Dismissal in Breast Cancer Screening](https://mlsanity.com/paper/2609.00300/) (2026)

## Abstract (arXiv)

Segmentation of cardiac anatomical structures in cardiac magnetic resonance images (CMRI) is a prerequisite for automatic diagnosis and prognosis of cardiovascular diseases. To increase robustness and performance of segmentation methods this study combines automatic segmentation and assessment of segmentation uncertainty in CMRI to detect image regions containing local segmentation failures. Three state-of-the-art convolutional neural networks (CNN) were trained to automatically segment cardiac anatomical structures and obtain two measures of predictive uncertainty: entropy and a measure derived by MC-dropout. Thereafter, using the uncertainties another CNN was trained to detect local segmentation failures that potentially need correction by an expert. Finally, manual correction of the detected regions was simulated. Using publicly available CMR scans from the MICCAI 2017 ACDC challenge, the impact of CNN architecture and loss function for segmentation, and the uncertainty measure was investigated. Performance was evaluated using the Dice coefficient and 3D Hausdorff distance between manual and automatic segmentation. The experiments reveal that combining automatic segmentation with simulated manual correction of detected segmentation failures leads to statistically significant performance increase.