Cited by 428Medical Image Segmentation TechniquesAdvanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.

Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation

Open access

Mehrtash, Alireza, Wells, William M., Tempany, Clare M., Abolmaesumi, Purang, Kapur, Tina·2020·IEEE Transactions on Medical Imaging

DOI 10.1109/tmi.2020.3006437PMC PMC7704933

Open this paper in CoChat — free account

Abstract

Fully convolutional neural networks (FCNs), and in particular U-Nets, have achieved state-of-the-art results in semantic segmentation for numerous medical imaging applications. Moreover, batch normalization and Dice loss have been used successfully to stabilize and accelerate training. However, these networks are poorly calibrated i.e. they tend to produce overconfident predictions for both correct and erroneous classifications, making them unreliable and hard to interpret. In this paper, we study predictive uncertainty estimation in FCNs for medical image segmentation. We make the following contributions: 1) We systematically compare cross-entropy loss with Dice loss in terms of segmentation quality and uncertainty estimation of FCNs; 2) We propose model ensembling for confidence calibration of the FCNs trained with batch normalization and Dice loss; 3) We assess the ability of calibrated FCNs to predict segmentation quality of structures and detect out-of-distribution test examples. We conduct extensive experiments across three medical image segmentation applications of the brain, the heart, and the prostate to evaluate our contributions. The results of this study offer considerable insight into the predictive uncertainty estimation and out-of-distribution detection in medical image segmentation and provide practical recipes for confidence calibration. Moreover, we consistently demonstrate that model ensembling improves confidence calibration.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1109/tmi.2020.3006437

Cites

56 of 56 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Untitled work
(2014) · Cited by 33,419
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Milletari, F., Navab, N., & Ahmadi, S.-A. (2016) · 2016 Fourth International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.79 · Cited by 11,931
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898
Ensemble methods in machine learning
Dietterich, T. G. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45014-9_1 · Cited by 8,408

Flagged first, then by citations

Show all 55 references

Research works printed but not linked: 10

2018, Tgs Salt Identification Challenge
2018, Grand Challenge on MR Brain Segmentation at MICCAI 2018
10.5555/3045118.3045167 · DOI
10.21236/ADA459827 · DOI
10.5555/3045390.3045502 · DOI
Liao, 2019, On modelling label uncertainty in deep neural networks: Automatic estimation of intra-observer variability in 2D echocardiography quality assessment, arXiv:1911.00674 · arXiv
10.1109/CVPR.2018.00464 · DOI
10.1201/9781420002690 · DOI
10.5555/3295222.3295309 · DOI
10.1007/978-3-030-32251-9_73 · DOI

Sorted by citations

Items 101–200 of 428

Deep learning for standardized, MRI-based quantification of subcutaneous and subfascial tissue volume for patients with lipedema and lymphedema
Nowak, S., Henkel, A., Theis, M., Luetkens, J. A., Geiger, S., Sprinkart, A. M., Pieper, C. C., & Attenberger, U. I. (2022) · European Radiology · doi:10.1007/s00330-022-09047-0 · Cited by 16
Cardiac surgery risk prediction using ensemble machine learning to incorporate legacy risk scores: A benchmarking study
Dong, T., Sinha, S., Zhai, B., Fudulu, D. P., Chan, J., Narayan, P., Judge, A., Caputo, M., Dimagli, A., Benedetto, U., et al. (2023) · Digital Health · doi:10.1177/20552076231187605 · Cited by 16
An Extreme Value Theory-Based Approach for Reliable Drone RF Signal Identification
Chen, Y., Zhu, L., Jiao, Y., Yao, C., Cheng, K., & Gu, Y. (2023) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2023.3327594 · Cited by 16
Quality assurance for automatically generated contours with additional deep learning
Isaksson, L. J., Summers, P. E., Bhalerao, A. H., Gandini, S., Raimondi, S., Pepa, M., Zaffaroni, M., Corrao, G., Mazzola, G. C., Rotondi, M., et al. (2022) · Insights into Imaging · doi:10.1186/s13244-022-01276-7 · Cited by 16
Estimating Model Performance Under Domain Shifts with Class-Specific Confidence Scores
Li, Z., Kamnitsas, K., Islam, M., Chen, C., & Glocker, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16449-1_66 · Cited by 16
UniSAL: Unified Semi-supervised Active Learning for histopathological image classification
Zhong, L., Qian, K., Liao, X., Huang, Z., Liu, Y., Zhang, S., & Wang, G. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103542 · Cited by 16
Structural uncertainty estimation for medical image segmentation
Yang, B., Zhang, X., Zhang, H., Li, S., Higashita, R., & Liu, J. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103602 · Cited by 15
Evaluation of uncertainty estimation methods in medical image segmentation: Exploring the usage of uncertainty in clinical deployment
Li, S., Yuan, M., Dai, X., & Zhang, C. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2025.102574 · Cited by 16
A deep registration method for accurate quantification of joint space narrowing progression in rheumatoid arthritis
Wang, H., Ou, Y., Fang, W., Ambalathankandy, P., Goto, N., Ota, G., Okino, T., Fukae, J., Sutherland, K. L., Ikebe, M., et al. (2023) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2023.102273 · Cited by 15
Leveraging Uncertainty Estimates to Improve Segmentation Performance in Cardiac MR
Arega, T. W., Bricq, S., & Mériaudeau, F. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87735-4_3 · Cited by 15
AHU-MultiNet: Adaptive loss balancing based on homoscedastic uncertainty in multi-task medical image segmentation network
Liu, S., Wang, H., Li, Y., Li, X., Cao, G., & Cao, W. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106157 · Cited by 15
Post hoc calibration of medical segmentation models
Rousseau, A.-J., Becker, T., Appeltans, S., Blaschko, M. B., & Valkenborg, D. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-06587-0 · Cited by 16
Ensemble of Deep Convolutional Neural Networks with Monte Carlo Dropout Sampling for Automated Image Segmentation Quality Control and Robust Deep Learning Using Small Datasets
Hann, E., Gonzales, R. A., Popescu, I. A., Zhang, Q., Ferreira, V. M., & Piechnik, S. K. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-80432-9_22 · Cited by 14
Limitations of Out-of-Distribution Detection in 3D Medical Image Segmentation
Vasiliuk, A., Frolova, D., Belyaev, M., & Shirokikh, B. (2023) · Journal of Imaging · doi:10.3390/jimaging9090191 · Cited by 14
A Hybrid of Generative and Discriminative Models Based on the Gaussian-Coupled Softmax Layer
Hayashi, H. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3358113 · Cited by 16
Uncertainty estimation using a 3D probabilistic U-Net for segmentation with small radiotherapy clinical trial datasets
Chlap, P., Min, H., Dowling, J., Field, M., Cloak, K., Leong, T., Lee, M. T., Chu, J., Tan, J., Tran, P., et al. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102403 · Cited by 14
Quantifying and visualising uncertainty in deep learning-based segmentation for radiation therapy treatment planning: What do radiation oncologists and therapists want?
Huet‐Dastarac, M., van Acht, N., Maruccio, F. C., van Aalst, J. E., van Oorschodt, J. C. J., Cnossen, F., Janssen, T. M., Brouwer, C. L., Montero, A. M. B., & Hurkmans, C. (2024) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2024.110545 · Cited by 18
A unified benchmark for the unknown detection capability of deep neural networks
Kim, J., Koo, J., & Hwang, S. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120461 · Cited by 18
Stratification by Tumor Grade Groups in a Holistic Evaluation of Machine Learning for Brain Tumor Segmentation
Prabhudesai, S., Wang, N. C., Ahluwalia, V., Huan, X., Bapuraj, J. R., Banovic, N., & Rao, A. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.740353 · Cited by 13
On Calibration of Object Detectors: Pitfalls, Evaluation and Baselines
Kuzucu, S., Öksüz, K., Sadeghi, J., & Dokania, P. K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72664-4_11 · Cited by 18
Lithofacies prediction driven by logging‐based Bayesian‐optimized ensemble learning: A case study of lacustrine carbonate reservoirs
Gu, Y., Zhang, S., Wang, W., Pan, L., Zhang, D., & Bao, Z. (2022) · Geophysical Prospecting · doi:10.1111/1365-2478.13258 · Cited by 12
Automated Segmentation of MRI White Matter Hyperintensities in 8421 Patients with Acute Ischemic Stroke
Kim, H., Ryu, W., Schellingerhout, D., Park, J.-H., Chung, J., Jeong, S., Gwak, D., Kim, B. J., Kim, J., Hong, K., et al. (2024) · American Journal of Neuroradiology · doi:10.3174/ajnr.a8418 · Cited by 12
Renal tumor segmentation, visualization, and segmentation confidence using ensembles of neural networks in patients undergoing surgical resection
Bachanek, S., Würzberg, P., Biggemann, L., Janssen, T. Y., Nietert, M. M., Lotz, J., Zeuschner, P., Maßmann, A., Uhlig, A., & Uhlig, J. (2024) · European Radiology · doi:10.1007/s00330-024-11026-6 · Cited by 13
Enhancing Cardiovascular Risk Prediction: Development of an Advanced Xgboost Model with Hospital-Level Random Effects
Dong, T., Oronti, I. B., Sinha, S., Freitas, A., Zhai, B., Chan, J., Fudulu, D. P., Caputo, M., & Angelini, G. D. (2024) · Bioengineering · doi:10.3390/bioengineering11101039 · Cited by 12
Robustness analysis framework for computations associated with building performance models and immersive virtual experiments
Chokwitthaya, C., Zhu, Y., & Mukhopadhyay, S. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2021.101401 · Cited by 12
A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation
Camarasa, R., Bos, D., Hendrikse, J., Nederkoorn, P. J., Kooi, M. E., van der Lugt, A., & de Bruijne, M. (2021) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2021-ec49 · Cited by 12
Robust Segmentation Models Using an Uncertainty Slice Sampling-Based Annotation Workflow
Chlebus, G., Schenk, A., Hahn, H. K., van Ginneken, B., & Meine, H. (2022) · IEEE Access · doi:10.1109/access.2022.3141021 · Cited by 12
Uncertainty estimation and evaluation of deformation image registration based convolutional neural networks
Rivetti, L., Studen, A., Sharma, M., Chan, J. W. S., & Jeraj, R. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad4c4f · Cited by 11
Uncertainty-aware multiple-instance learning for reliable classification: Application to optical coherence tomography
de Vente, C., van Ginneken, B., Hoyng, C. B., Klaver, C. C. W., & Sá‎nchez, C. I. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103259 · Cited by 14
Explaining predictive uncertainty by exposing second-order effects
Bley, F., Lapuschkin, S., Samek, W., & Montavon, G. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111171 · Cited by 11
Training Auxiliary Prototypical Classifiers for Explainable Anomaly Detection in Medical Image Segmentation
Cho, W., Park, J.-H., & Choo, J. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00265 · Cited by 11
Spatial and Temporal Deep Learning in Air-Coupled Ultrasonic Testing for Enabling NDE 4.0
Schmid, S., Dürrmeier, F., & Große, C. U. (2023) · Journal of Nondestructive Evaluation · doi:10.1007/s10921-023-00993-3 · Cited by 12
Using Uncertainty Estimation To Reduce False Positives In Liver Lesion Detection
Bhat, I., Kuijf, H. J., Cheplygina, V., & Pluim, J. P. W. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434119 · Cited by 11
Investigating deep learning model calibration for classification problems in mechanics
Mohammadzadeh, S., Prachaseree, P., & Lejeune, E. (2023) · Mechanics of Materials · doi:10.1016/j.mechmat.2023.104749 · Cited by 10
Maximum Entropy on Erroneous Predictions: Improving Model Calibration for Medical Image Segmentation
Larrazabal, A. J., Martínez, C. E., Dolz, J., & Ferrante, E. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43898-1_27 · Cited by 10
A hybrid AI method for lung cancer classification using explainable AI techniques
Shivwanshi, R. R., & Nirala, N. (2025) · Physica Medica · doi:10.1016/j.ejmp.2025.104985 · Cited by 12
Probabilistic spatial analysis in quantitative microscopy with uncertainty-aware cell detection using deep Bayesian regression
Gomariz, Á., Portenier, T., Nombela‐Arrieta, C., & Göksel, O. (2022) · Science Advances · doi:10.1126/sciadv.abi8295 · Cited by 12
Uncertainty Calibrations of Deep-Learning Schemes for Full-Wave Inverse Scattering Problems
He, S., Zhang, G., & Wei, Z. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3300351 · Cited by 10
Uncertainty-Aware Knowledge Distillation for Collision Identification of Collaborative Robots
Kwon, W., Jin, Y., & Lee, S. J. (2021) · Sensors · doi:10.3390/s21196674 · Cited by 10
HNT-AI: An Automatic Segmentation Framework for Head and Neck Primary Tumors and Lymph Nodes in FDG- PET/CT Images
Salahuddin, Z., Chen, Y., Zhong, X., Rad, N. M., Woodruff, H. C., & Lambin, P. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-27420-6_21 · Cited by 9
Intra- and Inter-expert Validation of an Automatic Segmentation Method for Fluid Regions Associated with Central Serous Chorioretinopathy in OCT Images
Gende, M., Castelo-Martínez, L., de Moura, J., Novo, J., & Ortega, M. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-023-00926-6 · Cited by 9
Enhancing deforestation monitoring in the Brazilian Amazon: A semi-automatic approach leveraging uncertainty estimation
Martinez, J. A. C., da Costa, G. A. O. P., Messias, C. G., Soler, L. D. S., Almeida, C. A., & Feitosa, R. Q. (2024) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2024.03.004 · Cited by 9
Robust optimization strategies for contour uncertainties in online adaptive radiation therapy
Smolders, A., Bengtsson, I., Forsgren, A. L., Lomax, A., Weber, D. C., Fredriksson, A., & Albertini, F. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad6526 · Cited by 9
Average Calibration Error: A Differentiable Loss for Improved Reliability in Image Segmentation
Barfoot, T., García-Peraza-Herrera, L. C., Glocker, B., & Vercauteren, T. K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72114-4_14 · Cited by 9
Applications of Generative Artificial Intelligence in Brain MRI Image Analysis for Brain Disease Diagnosis
Yao, Z., Wang, Z., Xie, W., Zhan, Y., Wu, X., Pei, Y., Pei, Y., Wang, Z., & Zhang, G. (2024) · Neuropharmacology and Therapy · doi:10.15212/npt-2024-0007 · Cited by 9
Confidence-Aware Ship Classification Using Contour Features in SAR Images
Hinai, A. A. A., & Raffaella Guida, A. (2025) · Remote Sensing · doi:10.3390/rs17010127 · Cited by 9
Quantifying uncertainty in economics policy predictions: A Bayesian & Monte Carlo based data-driven approach
Rahaman, S. U., & Abdul, M. J. (2025) · International Review of Financial Analysis · doi:10.1016/j.irfa.2025.104157 · Cited by 9
Automated real-time assessment of intracranial hemorrhage detection AI using an ensembled monitoring model (EMM)
Fang, Z., Johnston, A., Cheuy, L. Y., Na, H. S., Paschali, M., González, C., Armstrong, B. A., Koirala, A., Laurel, D., Campion, A., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02007-0 · Cited by 11
BUFNet: Boundary-aware and uncertainty-driven multi-modal fusion network for MR brain tumor segmentation
Zhou, T., Ruan, S., & Lei, B. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103855 · Cited by 9
Assessing clinical applicability of COVID-19 detection in chest radiography with deep learning
Pedrosa, J., Aresta, G., Ferreira, C., Carvalho, C., Silva, J., Sousa, P., Ribeiro, L. D. O. V., Mendonça, A. M., & Campilho, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-10568-3 · Cited by 10
Semantic Segmentation for Various Applications: Research Contribution and Comprehensive Review
Mazhar, M., Fakhar, S., & Rehman, Y. (2023) · doi:10.3390/engproc2023032021 · Cited by 9
Studying the Impact of Augmentations on Medical Confidence Calibration
Rao, A., Lee, J., & Aalami, O. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00260 · Cited by 10
Robust, Primitive, and Unsupervised Quality Estimation for Segmentation Ensembles
Kofler, F., Ezhov, I., Fidon, L., Pirkl, C. M., Paetzold, J. C., Burian, E., Pati, S., Husseini, M. E., Navarro, F., Shit, S., et al. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.752780 · Cited by 9
Automatic Identification of Segmentation Errors for Radiotherapy Using Geometric Learning
Henderson, E. G. A., Green, A., van Herk, M., & Osorio, E. M. V. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16443-9_31 · Cited by 9
Classification pattern of lacustrine carbonate diagenetic facies and logging-based data-driven prediction via a generalized and robust ensemble learning: A demonstration of pre-salt profile, santos basin
Gu, Y., Li, Y., Yang, Y., Xiao, B. J., Zhang, D., & Bao, Z. (2023) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2023.211543 · Cited by 9
A Novel Method for Medical Predictive Models in Small Data Using Out-of-Distribution Data and Transfer Learning
Jeong, I., Kim, Y. H., Cho, N., Gil, H., & Lee�, H. (2024) · Mathematics · doi:10.3390/math12020237 · Cited by 8
Evidential Uncertainty-Guided Mitochondria Segmentation for 3D EM Images
Shi, R., Duan, L., Huang, T., & Jiang, T. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i5.28287 · Cited by 9
UPNet: Uncertainty-Based Picking Deep Learning Network for Robust First Break Picking
Wang, H., Zhang, J., Wei, X., Li, L., Zhang, C., & Guo, Z. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3439685 · Cited by 9
Closer Look at the Uncertainty Estimation in Semantic Segmentation under Distributional Shift
Cygert, S., Wroblewski, B., Wozniak, K., Slowinski, R., & Czyżewski, A. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533330 · Cited by 8
Quality Quantification in Deep Convolutional Neural Networks for Skin Lesion Segmentation using Fuzzy Uncertainty Measurement
Qiao, L., Chen, X., Chen, C., & Garibaldi, J. M. (2022) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee55066.2022.9882728 · Cited by 8
Evidential time-to-event prediction with calibrated uncertainty quantification
Huang, L., Xing, Y., Mishra, S., Denœux, T., & Feng, M. (2025) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2025.109403 · Cited by 7
Focal difficult-to-predict pixels dice loss for mitigating data imbalance in medical image segmentation
Zhang, W., Chen, Y., Long, Z., Chen, H., Zhang, Y., Zhou, Z., Chen, W., & Le, X. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128518 · Cited by 7
FusionSegNet: A Hierarchical Multi-Axis Attention and gated feature fusion network for breast lesion segmentation with uncertainty modeling in ultrasound imaging
Ahmed, M. R., & Lasserre, P. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103399 · Cited by 8
The impact of uncertainty estimation on radiomic segmentation reproducibility and scan–rescan repeatability in kidney MRI
Damiano, R., Lanzarone, E., Lussana, F., Villa, G., Daniel, A. J., Francis, S., Caroli, A., & Scalco, E. (2025) · Medical Physics · doi:10.1002/mp.17995 · Cited by 8
A novel uncertainty-aware point cloud approach for geometric quality monitoring in construction
Luo, H., Zhan, J., Liu, J., Zhou, W., & Fang, W. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103642 · Cited by 7
SicTTA: Single image continual test time adaptation for medical image segmentation
Wu, J., Liu, X., Wang, G., & Zhang, S. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103859 · Cited by 7
Can uncertainty estimation predict segmentation performance in ultrasound bone imaging?
Pandey, P. U., Guy, P., & Hodgson, A. J. (2022) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-022-02597-0 · Cited by 7
Informative and Reliable Tract Segmentation for Preoperative Planning
Lucena, O., Borges, P., Cardoso, M. J., Ashkan, K., Sparks, R. E., & Sebastien Ourselin, A. (2022) · Frontiers in Radiology · doi:10.3389/fradi.2022.866974 · Cited by 8
Evaluation of mediastinal lymph node segmentation of heterogeneous CT data with full and weak supervision
Mehrtash, A., Ziegler, E., Idris, T., Somarouthu, B., Urban, T., LaCasce, A. S., Jacene, H. A., den Abbeele, A. D. V., Pieper, S., Harris, G. J., et al. (2023) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2023.102312 · Cited by 7
Self-citation
Leveraging voxel-wise segmentation uncertainty to improve reliability in assessment of paediatric dysplasia of the hip
Kannan, A., Hodgson, A. J., Mulpuri, K., & Garbi, R. (2021) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-021-02389-y · Cited by 7
U-Net-based medical image segmentation algorithm
Yan, W., Chen, C., & Zhang, D. (2021) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp52459.2021.9613447 · Cited by 7
Ensemble learning and test-time augmentation for the segmentation of mineralized cartilage versus bone in high-resolution microCT images
Léger, J., Leyssens, L., Kerckhofs, G., & Vleeschouwer, C. D. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105932 · Cited by 7
Modeling Annotator Variation and Annotator Preference for Multiple Annotations Medical Image Segmentation
Guo, X., Lu, S., Yang, Y., Shi, P., Ye, C., Xiang, Y., & Ma, T. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995619 · Cited by 7
Medical Boundary Diffusion Model for Skin Lesion Segmentation
Wang, J., Yang, J., Zhou, Q., & Wang, L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43901-8_41 · Cited by 8
Benchmarking Scalable Epistemic Uncertainty Quantification in Organ Segmentation
Adams, J., & Elhabian, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44336-7_6 · Cited by 7
Improving Calibration and Out-of-Distribution Detection in Medical Image Segmentation with Convolutional Neural NetworksPreprint
Karimi, D., & Gholipour, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.06569 · Cited by 7
Trustworthy Medical Segmentation with Uncertainty Estimation.Preprint
Carannante, G., Dera, D., Bouaynaya, N. C., Rasool, G., & Fathallah‐Shaykh, H. M. (2021) · arXiv (Cornell University) · Cited by 7
Calibration for Long-tailed Scene Graph Generation
Zhu, X., Xing, Y., Wang, R., Wang, Y., & Lan, X. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680818 · Cited by 6
Comprehensive exploiting local and global features for brain tumor segmentation: A gated dual-branch hybrid attention mechanism
Zhou, Z., Yu, X., Huang, W., Qiu, S., Humayoo, M., Li, H., & Shi, M. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108285 · Cited by 6
Exploring foundation models for multi-class muscle segmentation in MR images of neuromuscular disorders: A comparative analysis of accuracy and uncertainty
Casali, N., Brusaferri, A., Baselli, G., Moscatelli, M. E. M., Aquino, D., Grisoli, M., Rizzo, G., & Mastropietro, A. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109035 · Cited by 7
Vision-Based Uncertainty-Aware Motion Planning Based on Probabilistic Semantic Segmentation
Römer, R., Lederer, A., Tesfazgi, S., & Hirche, S. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3322899 · Cited by 6
Adaptive window adjustment with boundary DoU loss for cascade segmentation of anatomy and lesions in prostate cancer using bpMRI
Li, W., Zheng, B., Shen, Q., Shi, X., Luo, K., Yao, Y., Li, X., Lv, S., Tao, J., & Wei, Q. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106831 · Cited by 6
RAE-Net: a multi-modal neural network based on feature fusion and evidential deep learning algorithm in predicting breast cancer subtypes on DCE-MRI
Tang, X., & Zhu, Y. (2025) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/adb494 · Cited by 9
Neighbor-aware calibration of segmentation networks with penalty-based constraints
Murugesan, B., Vasudeva, S. A., Liu, B., Lombaert, H., Ayed, I. B., & Dolz, J. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103501 · Cited by 10
Dual-channel compression mapping network with fused attention mechanism for medical image segmentation
Ding, X., Qian, K., Zhang, Q., Jiang, X., & Dong, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-93494-4 · Cited by 6
Image Segmentation Techniques for Intelligent Monitoring of Putonghua Examinations
Liu, H. (2022) · Advances in Mathematical Physics · doi:10.1155/2022/4302666 · Cited by 6
Convolution Feature Inference-Based Semantic Understanding Method for Remote Sensing Images of Mangrove Forests
Wu, S., Zhao, Y., Wang, Y., Chen, J., Zang, T., & Chen, H. (2023) · Electronics · doi:10.3390/electronics12040881 · Cited by 6
Influence of uncertainty estimation techniques on false-positive reduction in liver lesion detection
Bhat, I., Pluim, J. P. W., Viergever, M. A., & Kuijf, H. J. (2022) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2022-5937 · Cited by 8
MRI Denoising Neural Network Architecture Convolution
Sliusarenko, D., Нетреба, А. В., & Radchenko, S. P. (2023) · IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) · doi:10.1109/idaacs58523.2023.10348629 · Cited by 6
Glioma Sub-region Segmentation on Multi-parameter MRI with Label Dropout
Cheng, K., Hu, C., Yin, P., Su, Q., Zhou, G., Wu, X., Wang, X., & Wei, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72087-2_37 · Cited by 6
Crowdsourcing More Effective Initializations for Single-Target Trackers Through Automatic Re-querying
Lemmer, S. J., Song, J. Y., & Corso, J. J. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445181 · Cited by 6
Robust Object Detection with Multi-input Multi-output Faster R-CNN
Cygert, S., & Czyżewski, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-06427-2_48 · Cited by 6
Uncertainty estimates for semantic segmentation: providing enhanced reliability for automated motor claims handling
Küchler, J., Kröll, D., Schoenen, S., & Witte, A. (2024) · Machine Vision and Applications · doi:10.1007/s00138-024-01541-3 · Cited by 7
Uncertainty Driven Adaptive Self-Knowledge Distillation for Medical Image Segmentation
Guo, X., Wang, M., Xiang, Y., Yang, Y., Ye, C., Wang, H., & Ma, T. (2025) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2025.3526259 · Cited by 5
DistillSleep : real-time, on-device, interpretable sleep staging from single-channel electroencephalogram
Park, K., Hong, J., Lee, W., Shin, H., & Kim, H. (2025) · SLEEP · doi:10.1093/sleep/zsaf240 · Cited by 5
Confidence in radiology AI: From black-box scores to trusted decisions
Karunanayake, N. (2025) · European Journal of Radiology Artificial Intelligence · doi:10.1016/j.ejrai.2025.100046 · Cited by 5
A Multimodal Feature Distillation With Mamba-Transformer Network for Brain Tumor Segmentation With Incomplete Modalities
Kang, M., Ting, F. F., Saw, S. N., Phan, R. C. W., Ge, Z., & Ting, C. (2026) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2026.3678550 · Cited by 5
Uncertainty quantification via localized gradients for deep learning-based medical image assessments
Schott, B., Pinchuk, D., Santoro-Fernandes, V., Klaneček, Ž., Rivetti, L., Deatsch, A. E., Perlman, S. B., Li, Y., & Jeraj, R. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad611d · Cited by 5
Post-hoc out-of-distribution detection for cardiac MRI segmentation
Arega, T. W., Bricq, S., & Mériaudeau, F. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102476 · Cited by 5
Uncertainty-aware self-training with adversarial data augmentation for semi-supervised medical image segmentation
Cao, J., Chen, J., Liu, J., Gu, Y., & Chen, L. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107561 · Cited by 5

Authors: Mehrtash, A., Wells, W. M., Tempany, C. M., Abolmaesumi, P., & Kapur, T.
Affiliations: Brigham and Women's Hospital (US)
University of British Columbia (CA)
ORCID: Mehrtash, Alireza
Wells, William M.
Abolmaesumi, Purang
Kapur, Tina
Venue: IEEE Transactions on Medical Imaging
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2020-12
Volume: 39
Issue: 12
Pages: 3868-3878
ISSN: 0278-0062 (ISSN-L)
1558-254X
DOI: 10.1109/tmi.2020.3006437
PMC: PMC7704933
OpenAlex: W2991523350
Cited by: 428
References: 55
Citations by year: 2026: 91 · 2025: 92 · 2024: 71 · 2023: 71 · 2022: 56 · 2021: 41 · 2020: 6
FWCI: 16.91
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Medical Imaging (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: U.S. National Institutes of Health (P41EB015898)
Natural Sciences and Engineering Research Council (NSERC) of Canada
Canadian Institutes of Health Research
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords: confidence calibration, predictive uncertainty estimation, medical image segmentation, fully convolutional network, U-Net, Dice loss, cross-entropy loss, model ensembling, out-of-distribution detection, batch normalization, brain segmentation, cardiac image segmentation, prostate segmentation
MeSH terms: Brain, Calibration, Humans, Image Processing, Computer-Assisted, Male, Neural Networks, Computer, Uncertainty

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks