# Efficient Model Monitoring for Quality Control in Cardiac Image Segmentation

arXiv: https://arxiv.org/abs/2104.05533 · HTML: https://mlsanity.com/paper/2104.05533/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 12 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders](https://mlsanity.com/paper/1807.02011/) (2018)
- [Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation](https://mlsanity.com/paper/1705.08302/) (2017)
- [Cardiac Segmentation with Strong Anatomical Guarantees](https://mlsanity.com/paper/2006.08825/) (2020)
- [An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation](https://mlsanity.com/paper/1709.04496/) (2017)
- [Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth](https://mlsanity.com/paper/1702.03407/) (2017)
- [Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors](https://mlsanity.com/paper/1906.07347/) (2019)
- [Real-time Prediction of Segmentation Quality](https://mlsanity.com/paper/1806.06244/) (2018)
- [Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with Bayesian inference for uncertainty-based quality-control](https://mlsanity.com/paper/2001.11711/) (2020)

## Built on by (most cited first, 7 of 7)

- [ConfIC-RCA: Statistically Grounded Efficient Estimation of Segmentation Quality](https://mlsanity.com/paper/2503.04522/) (2025)
- [Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data](https://mlsanity.com/paper/2109.07955/) (2021)
- [FUSQA: Fetal Ultrasound Segmentation Quality Assessment](https://mlsanity.com/paper/2303.04418/) (2023)
- [SingleStrip: learning skull-stripping from a single labeled example](https://mlsanity.com/paper/2508.10464/) (2025)
- [A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis](https://mlsanity.com/paper/2205.01673/) (2022)
- [An AI tool for automated analysis of large-scale unstructured clinical cine CMR databases](https://mlsanity.com/paper/2206.08137/) (2022)
- [MID: Mask-Image Distributional Divergence for Evaluating Medical Image Segmentation](https://mlsanity.com/paper/2610.17354/) (2026)

## Abstract (arXiv)

Deep learning methods have reached state-of-the-art performance in cardiac image segmentation. Currently, the main bottleneck towards their effective translation into clinics requires assuring continuous high model performance and segmentation results. In this work, we present a novel learning framework to monitor the performance of heart segmentation models in the absence of ground truth. Formulated as an anomaly detection problem, the monitoring framework allows deriving surrogate quality measures for a segmentation and allows flagging suspicious results. We propose two different types of quality measures, a global score and a pixel-wise map. We demonstrate their use by reproducing the final rankings of a cardiac segmentation challenge in the absence of ground truth. Results show that our framework is accurate, fast, and scalable, confirming it is a viable option for quality control monitoring in clinical practice and large population studies.