mlsanityData to 7 Oct 2026

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv 2112.10074 ↗First posted 19 Dec 2021eess.IV cs.CV cs.LGcited by 28

Not on the map. Why?

Abstract (arXiv)

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at: https://github.com/RagMeh11/QU-BraTS.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Builds on 50 of its 97 references that are in the map. First posted on arXiv 19 Dec 2021.

Builds on

50 of 97 references.

References · 50

  1. 2016Densely Connected Convolutional Networkscited by 6,691
  2. 2017On Calibration of Modern Neural Networkscited by 4,860
  3. 2017Squeeze-and-Excitation Networkscited by 4,763
  4. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  5. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  6. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  7. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  8. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  9. 2016Variational Inference: A Review for Statisticianscited by 1,466
  10. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 813
  11. 2017Deep Bayesian Active Learning with Image Datacited by 812
  12. 2017Focal Loss for Dense Object Detectioncited by 749
38 more
  1. 2019Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)cited by 657
  2. 2021The Medical Segmentation Decathloncited by 620
  3. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 508
  4. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 507
  5. 2018Confounding variables can degrade generalization performance of radiological deep learning modelscited by 374
  6. 2019A large annotated medical image dataset for the development and evaluation of segmentation algorithmscited by 371
  7. 2020BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learningcited by 368
  8. 2018A Probabilistic U-Net for Segmentation of Ambiguous Imagescited by 281
  9. 2016Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentationcited by 263
  10. 2019REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographscited by 226
  11. 2015Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understandingcited by 218
  12. 2020Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learningcited by 208
  13. 2019The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challengecited by 188
  14. 2018Understanding Measures of Uncertainty for Adversarial Example Detectioncited by 177
  15. 2018Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentationcited by 146
  16. 2018Evaluating Bayesian Deep Learning Methods for Semantic Segmentationcited by 143
  17. 2019PHiSeg: Capturing Uncertainty in Medical Image Segmentationcited by 128
  18. 2018Unsupervised Learning for Fast Probabilistic Diffeomorphic Registrationcited by 116
  19. 2020Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertaintycited by 100
  20. 2018No New-Netcited by 61
  21. 2021Common Limitations of Image Processing Metrics: A Picture Storycited by 58
  22. 2018Probabilistic Object Detection: Definition and Evaluationcited by 55
  23. 2021The Federated Tumor Segmentation (FeTS) Challengecited by 49
  24. 2020Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challengecited by 28
  25. 2019DRvertGRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus imagescited by 25
  26. 2019Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessmentcited by 23
  27. 2018Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognitioncited by 22
  28. 2020Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIscited by 12
  29. 2020Uncertainty Evaluation Metric for Brain Tumour Segmentationcited by 11
  30. 2019TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networkscited by 8
  31. 2020Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentationcited by 8
  32. 2020Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertaintycited by 6
  33. 2020MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architecturescited by 6
  34. 2019DeepMRSeg: A convolutional deep neural network for anatomy and abnormality segmentation on MR imagescited by 5
  35. 2021HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Imagescited by 5
  36. 2021A Decoupled Uncertainty Model for MRI Segmentation Quality Estimationcited by 3
  37. 2019The Probabilistic Object Detection Challengecited by 2
  38. 2019Multi-Resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Predictioncited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 7; 2023: 7; 2024: 3; 2025: 6; 2026: 5.

28 papers.

Sort:

By area:

  1. 2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysiscited by 62
  2. 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
  3. 2022Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentationcited by 20
  4. 2023Evaluating the Fairness of Deep Learning Uncertainty Estimates in Medical Image Analysiscited by 15
  5. 2023Anatomically-aware Uncertainty for Semi-supervised Image Segmentationcited by 13
  6. 2022Novel structural-scale uncertainty measures and error retention curves: application to multiple sclerosiscited by 8
  7. 2024Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenescited by 8
  8. 2023Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentationcited by 7
  9. 2022Uncertainty Estimation for Heatmap-based Landmark Localizationcited by 6
  10. 2023A Radiomics-Incorporated Deep Ensemble Learning Model for Multi-Parametric MRI-based Glioma Segmentationcited by 4
  11. 2023Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensemblescited by 4
  12. 2022USE-Evaluator: Performance Metrics for Medical Image Segmentation Models with Uncertain, Small or Empty Reference Annotationscited by 3
16 more
  1. 2022Quantifying U-Net Uncertainty in Multi-Parametric MRI-based Glioma Segmentation by Spherical Image Projectioncited by 3
  2. 2025Average Calibration Losses for Reliable Uncertainty in Medical Image Segmentationcited by 3
  3. 2022Multi-rater Prism: Learning self-calibrated medical image segmentation from multiple raterscited by 2
  4. 2024MultiOrg: A Multi-rater Organoid-detection Datasetcited by 2
  5. 2025The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learningcited by 2
  6. 2023Why does my medical AI look at pictures of birds? Exploring the efficacy of transfer learning across domain boundariescited by 1
  7. 2024QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Predictioncited by 0
  8. 2025ZECO: ZeroFusion Guided 3D MRI Conditional Generationcited by 0
  9. 2025UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature Fusioncited by 0
  10. 2025UNSURF: Uncertainty Quantification for Cortical Surface Reconstruction of Clinical Brain MRIscited by 0
  11. 2025Enhancing Neuro-Oncology Through Self-Assessing Deep Learning Models for Brain Tumor Unified Model for MRI Segmentationcited by 0
  12. 2026Med-SegLens: Latent-Level Model Diffing for Interpretable Medical Image Segmentationcited by 0
  13. 2026Confidence is Not Reliability: Rethinking MC Dropout in Brain Tumour Segmentationcited by 0
  14. 2026DAMamba-UNet3D: A Parameter-Efficient Mamba State Space U-Net with Dynamic Adaptive Scan for 3D Medical Image Segmentationcited by 0
  15. 2026Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertaintycited by 0
  16. 2026When Fusion Fails: Corruption-Aware Rebalanced Fusion for Multi-Modal Medical Image Segmentationcited by 0