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References from Uncertainty-aware auto-segmentation enables adaptive radiotherapy planning of liver metastases. Local targets link to admitted publications; unresolved targets remain external evidence.
Clinical implementation and evaluation of auto-segmentation tools for multi-site contouring in radiotherapy
2023 · External reference
The liver tumor segmentation benchmark (LiTS)
10.1016/j.media.2022.102680 · 2023 · External reference
Deep visual domain adaptation: a survey
10.1016/j.neucom.2018.05.083 · 2018 · External reference
Trustworthy clinical AI solutions: a unified review of uncertainty quantification in Deep Learning models for medical image analysis
10.1016/j.artmed.2024.102830 · 2024 · External reference
Reliability of uncertainty quantification methods for deep learning auto-segmentation in head and neck organs at risk
10.1088/1361-6560/ae110c · 2025 · External reference
Artificial intelligence uncertainty quantification in radiotherapy applications − a scoping review
10.1016/j.radonc.2024.110542 · 2024 · External reference
Understanding and leveraging uncertainties in autosegmentation for radiotherapy
2025 · External reference
Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation
10.1016/j.compbiomed.2023.107096 · 2023 · External reference
Uncertainty-error correlations in evidential deep learning models for biomedical segmentation
2024 · External reference
Evaluating uncertainty quantification in medical image segmentation: a multi-dataset, multi-algorithm study
10.3390/app142110020 · 2024 · External reference
The impact of uncertainty estimation on radiomic segmentation reproducibility and scan-rescan repeatability in kidney MRI
10.1002/mp.17995 · 2025 · External reference
10.1007/978-3-030-00928-1_78
10.1007/978-3-030-00928-1_78 · External reference
Unresolved reference
External reference
10.1038/s41592-020-01008-z · 2021 · Admitted local publication
Kidney tumor semantic segmentation using deep learning: a survey of state-of-the-art
10.3390/jimaging8030055 · 2022 · External reference
Prospective image quality and lesion assessment in the setting of MR-guided radiation therapy of prostate cancer on an MR-Linac at 1.5 T: a comparison to a standard 3 T MRI
10.3390/cancers13071533 · 2021 · External reference
Research on liver cancer segmentation method based on PCNN image processing and SE-ResUnet
10.1038/s41598-023-39240-0 · 2023 · External reference
A novel unified Inception-U-Net hybrid gravitational optimization model (UIGO) incorporating automated medical image segmentation and feature selection for liver tumor detection
10.1038/s41598-025-14333-0 · 2025 · External reference
Unresolved reference
External reference
Selecting the best optimizers for deep learning-based medical image segmentation
10.3389/fradi.2023.1175473 · 2023 · External reference
10.1007/978-3-031-16452-1_51
10.1007/978-3-031-16452-1_51 · External reference
Confidence calibration and predictive uncertainty estimation for deep medical image segmentation
10.1109/tmi.2020.3006437 · 2020 · External reference
Analyzing the quality and challenges of uncertainty estimations for brain tumor segmentation
2020 · External reference
Liver metastases: the role of magnetic resonance imaging
10.3748/wjg.v29.i36.5180 · 2023 · External reference
Shape-constraint region growing for delineation of hepatic metastases on contrast-enhanced computed tomograph scans
10.1097/01.rli.0000236907.81400.18 · 2006 · External reference
Evaluation of measures for assessing time-saving of automatic organ-at-risk segmentation in radiotherapy
10.1016/j.phro.2019.12.001 · 2020 · External reference
Deep learning application for abdominal organs segmentation on 0.35 T MR-Linac images
10.3389/fonc.2023.1285924 · 2024 · External reference
Cardiac magnetic resonance image segmentation based on convolutional neural network
10.1016/j.cmpb.2020.105755 · 2020 · External reference
Automated segmentation method for low field 3D stomach MRI using transferred learning image enhancement network
2021 · External reference
Automatic segmentation of liver structures in multi-phase MRI using variants of nnU-net and Swin UNETR
10.1038/s41598-025-07084-5 · 2025 · External reference
A deep-learning approach for segmentation of liver tumors in magnetic resonance imaging using UNet+
10.1186/s12885-023-11432-x · 2023 · External reference
A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods
10.1016/j.media.2024.103223 · 2024 · External reference
Uncertainty quantification in multi-class segmentation: comparison between Bayesian and non-Bayesian approaches in a clinical perspective
10.1002/mp.17189 · 2024 · External reference
Enhancing monte carlo dropout performance for uncertainty quantification
10.1007/s00521-026-12165-1 · 2026 · External reference
Simple and scalable predictive uncertainty estimation using deep ensembles
2017 · External reference
A review of entropy measures for uncertainty quantification of stochastic processes
10.1177/1687814019857350 · 2019 · External reference
Evidential uncertainty quantification: a variance-based perspective
2024 · External reference
Automatic segmentation with detection of local segmentation failures in cardiac MRI
10.1038/s41598-020-77733-4 · 2020 · External reference
