# Real-time Prediction of Segmentation Quality

arXiv: https://arxiv.org/abs/1806.06244 · HTML: https://mlsanity.com/paper/1806.06244/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2018.

## Built on by (most cited first, 23 of 23)

- [Uncertainty-aware Multi-modal Learning via Cross-modal Random Network Prediction](https://mlsanity.com/paper/2207.10851/) (2022)
- [Comparative Benchmarking of Failure Detection Methods in Medical Image Segmentation: Unveiling the Role of Confidence Aggregation](https://mlsanity.com/paper/2406.03323/) (2024)
- [Estimating Model Performance under Domain Shifts with Class-Specific Confidence Scores](https://mlsanity.com/paper/2207.09957/) (2022)
- [Uncertainty-Aware Temporal Self-Learning (UATS): Semi-Supervised Learning for Segmentation of Prostate Zones and Beyond](https://mlsanity.com/paper/2104.03840/) (2021)
- [FSNet: A Failure Detection Framework for Semantic Segmentation](https://mlsanity.com/paper/2108.08748/) (2021)
- [Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse](https://mlsanity.com/paper/2104.08131/) (2021)
- [Conformal Performance Range Prediction for Segmentation Output Quality Control](https://mlsanity.com/paper/2407.13307/) (2024)
- [Efficient Model Monitoring for Quality Control in Cardiac Image Segmentation](https://mlsanity.com/paper/2104.05533/) (2021)
- [Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration](https://mlsanity.com/paper/2111.07716/) (2021)
- [ConfIC-RCA: Statistically Grounded Efficient Estimation of Segmentation Quality](https://mlsanity.com/paper/2503.04522/) (2025)
- [Post-hoc Models for Performance Estimation of Machine Learning Inference](https://mlsanity.com/paper/2110.02459/) (2021)
- [Towards Ground-truth-free Evaluation of Any Segmentation in Medical Images](https://mlsanity.com/paper/2409.14874/) (2024)
- [Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data](https://mlsanity.com/paper/2109.07955/) (2021)
- [Quality Sentinel: Estimating Label Quality and Errors in Medical Segmentation Datasets](https://mlsanity.com/paper/2406.00327/) (2024)
- [Hardware-aware Real-time Myocardial Segmentation Quality Control in Contrast Echocardiography](https://mlsanity.com/paper/2109.06909/) (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)
- [Cross-Model Agreement as a Deployment-Time Reliability Signal for Automatic Polyp Segmentation](https://mlsanity.com/paper/2609.10495/) (2026)
- [Deep Learning Pipeline for Preprocessing and Segmenting Cardiac Magnetic Resonance of Single Ventricle Patients from an Image Registry](https://mlsanity.com/paper/2303.11676/) (2023)
- [A quality assurance framework for real-time monitoring of deep learning segmentation models in radiotherapy](https://mlsanity.com/paper/2305.11715/) (2023)
- [Machine Learning Techniques for MRI Data Processing at Expanding Scale](https://mlsanity.com/paper/2404.14326/) (2024)
- [QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Prediction](https://mlsanity.com/paper/2412.07156/) (2024)
- [Can Frozen Hyperspherical Features Guide the Selection of Pseudo Masks?](https://mlsanity.com/paper/2609.30080/) (2026)

## Abstract (arXiv)

Recent advances in deep learning based image segmentation methods have enabled real-time performance with human-level accuracy. However, occasionally even the best method fails due to low image quality, artifacts or unexpected behaviour of black box algorithms. Being able to predict segmentation quality in the absence of ground truth is of paramount importance in clinical practice, but also in large-scale studies to avoid the inclusion of invalid data in subsequent analysis. In this work, we propose two approaches of real-time automated quality control for cardiovascular MR segmentations using deep learning. First, we train a neural network on 12,880 samples to predict Dice Similarity Coefficients (DSC) on a per-case basis. We report a mean average error (MAE) of 0.03 on 1,610 test samples and 97% binary classification accuracy for separating low and high quality segmentations. Secondly, in the scenario where no manually annotated data is available, we train a network to predict DSC scores from estimated quality obtained via a reverse testing strategy. We report an MAE=0.14 and 91% binary classification accuracy for this case. Predictions are obtained in real-time which, when combined with real-time segmentation methods, enables instant feedback on whether an acquired scan is analysable while the patient is still in the scanner. This further enables new applications of optimising image acquisition towards best possible analysis results.