Automatic Quality Control of Cardiac MRI Segmentation in Large-Scale Population Imaging

UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age

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A Probabilistic Patch-Based Label Fusion Model for Multi-Atlas Segmentation With Registration Refinement: Application to Cardiac MR Images

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Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth

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Automated Image Analysis for High-Content Screening and Analysis

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Cross Validation Framework to Choose amongst Models and Datasets for Transfer Learning

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Evaluating Segmentation Error without Ground Truth

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Encoding atlases by randomized classification forests for efficient multi-atlas label propagation

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Automated Quality Assessment of Cardiac MR Images Using Convolutional Neural Networks

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Towards the Semantic Enrichment of Free-Text Annotation of Image Quality Assessment for UK Biobank Cardiac Cine MRI Scans

Carapella, V., Jiménez-Ruiz, E., Lukaschuk, E. I., Aung, N., Fung, K., Paiva, J. M., Sanghvi, M. M., Neubauer, S., Petersen, S. E., Horrocks, I. R., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46976-8_25 · Cited by 14 · see citing works

Fully Automated, Quality-Controlled Cardiac Analysis From CMR

Ruijsink, B., Anton, E. P., Öksüz, İ., Sinclair, M. D., Bai, W., Schnabel, J. A., Razavi, R., & King, A. P. (2019) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2019.05.030 · Cited by 185 · see citing works

Bayesian QuickNAT: model uncertainty in deep whole-brain segmentation for structure-wise quality control

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Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study

Robinson, R., Valindria, V. V., Bai, W., Oktay, O., Kainz, B., Suzuki, H., Sanghvi, M. M., Aung, N., Paiva, J. M., Zemrak, F., et al. (2019) · Journal of Cardiovascular Magnetic Resonance · doi:10.1186/s12968-019-0523-x · Cited by 120 · see citing works

Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning

Öksüz, İ., Ruijsink, B., Anton, E. P., Clough, J. R., da Cruz, G. J. L., Bustin, A., Prieto, C., Botnar, R. M., Rueckert, D., Schnabel, J. A., et al. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.04.009 · Cited by 102 · see citing works

Deep Learning-Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation

Öksüz, İ., Clough, J. R., Ruijsink, B., Anton, E. P., Bustin, A., da Cruz, G. J. L., Prieto, C., King, A. P., & Schnabel, J. A. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3008930 · Cited by 100 · see citing works

Computational anatomy for multi-organ analysis in medical imaging: A review

Cerrolaza, J. J., Picazo, M. L., Humbert, L., Sato, Y., Rueckert, D., Ballester, M. Á. G., & Linguraru, M. G. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.04.002 · Cited by 89 · see citing works

Deep neural network ensemble for on-the-fly quality control-driven segmentation of cardiac MRI T1 mapping

Hann, E., Popescu, I. A., Zhang, Q., Gonzales, R. A., Barutçu, A., Neubauer, S., Ferreira, V. M., & Piechnik, S. K. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102029 · Cited by 81 · see citing works

Real-Time Prediction of Segmentation Quality

Robinson, R., Oktay, O., Bai, W., Valindria, V. V., Sanghvi, M. M., Aung, N., Paiva, J. M., Zemrak, F., Fung, K., Lukaschuk, E. I., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00937-3_66 · Cited by 61 · see citing works

Automated localization and quality control of the aorta in cine CMR can significantly accelerate processing of the UK Biobank population data

Biasiolli, L., Hann, E., Lukaschuk, E. I., Carapella, V., Paiva, J. M., Aung, N., Rayner, J. J., Werys, K., Fung, K., Puchta, H., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212272 · Cited by 41 · see citing works

Large-scale Quality Control of Cardiac Imaging in Population Studies: Application to UK Biobank

Tarroni, G., Bai, W., Oktay, O., Schuh, A., Suzuki, H., Glocker, B., Matthews, P. M., & Rueckert, D. (2020) · Scientific Reports · doi:10.1038/s41598-020-58212-2 · Cited by 34 · see citing works

Deep Learning Using K-Space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection

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Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging

Hann, E., Biasiolli, L., Zhang, Q., Popescu, I. A., Werys, K., Lukaschuk, E. I., Carapella, V., Paiva, J. M., Aung, N., Rayner, J. J., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32245-8_83 · Cited by 29 · see citing works

An Alarm System for Segmentation Algorithm Based on Shape Model

Liu, F., Xia, Y., Yang, D., Yuille, A., & Xu, D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01075 · Cited by 25 · see citing works

Medical Image Computing and Computer Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part I

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Radiomics classifier to quantify automatic segmentation quality of cardiac sub-structures for radiotherapy treatment planning

Maffei, N., Manco, L., Aluisio, G., D’Angelo, E., Ferrazza, P., Vanoni, V., Meduri, B., Lohr, F., & Guidi, G. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.05.009 · Cited by 20 · see citing works

A framework for quality control of corpus callosum segmentation in large-scale studies

Herrera, W. G., Pereira, M. B., Bento, M., Lapa, A. T., Appenzeller, S., & Rittner, L. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108593 · Cited by 18 · see citing works

U-LanD: Uncertainty-Driven Video Landmark Detection

Jafari, M. H., Luong, C., Tsang, M., Gu, A. N., Woudenberg, N. V., Rohling, R., Tsang, T., & Abolmaesumi, P. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3123547 · Cited by 19 · see citing works

A probabilistic deep motion model for unsupervised cardiac shape anomaly assessment

Zakeri, A., Hokmabadi, A., Ravikumar, N., Frangi, A. F., & Gooya, A. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102276 · Cited by 15 · see citing works

Quality-driven deep active learning method for 3D brain MRI segmentation

Zhang, Z., Li, J., Tian, C.-N., Zhong, Z.-S., Jiao, Z., & Gao, X.-B. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.050 · Cited by 14 · see citing works

Image Quality Evaluation in Clinical Research: A Case Study on Brain and Cardiac MRI Images in Multi-Center Clinical Trials

Osadebey, M. E., Pedersen, M., Arnold, D. L., & Wendel-Mitoraj, K. E. (2018) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2018.2855213 · Cited by 13 · see citing works

A Fine-Grain Error Map Prediction and Segmentation Quality Assessment Framework for Whole-Heart Segmentation

Zhang, R., & Chung, A. C. S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32245-8_61 · Cited by 12 · see citing works

Automatic left ventricular outflow tract classification for accurate cardiac MR planning

Öksüz, İ., Ruijsink, B., Anton, E. P., Sinclair, M. D., Rueckert, D., Schnabel, J. A., & King, A. P. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363616 · Cited by 12 · see citing works

Real-time Prediction of Segmentation QualityPreprint

Robinson, R. H., Oktay, O., Bai, W., Valindria, V. V., Sanghvi, M. M., Aung, N., Paiva, J. M., Zemrak, F., Fung, K., Lukaschuk, E. I., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.06244 · Cited by 12 · see citing works

Robust Image Segmentation Quality AssessmentPreprint

Zhou, L., Deng, W., & Wu, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.08773 · Cited by 12 · see citing works

Subject-level Prediction of Segmentation Failure using Real-Time Convolutional Neural Nets

Robinson, R. H., Oktay, O., Bai, W., Valindria, V. V., Sanghvi, M. M., Aung, N., Paiva, J. M., Zemrak, F., Fung, K., Lukaschuk, E. I., et al. (2018) · Cited by 11 · see citing works

Segmentation evaluation with sparse ground truth data: Simulating true segmentations as perfect/imperfect as those generated by humans

Li, J., Udupa, J. K., Tong, Y., Wang, L., & Torigian, D. A. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101980 · Cited by 10 · see citing works

Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views

Wei, D., Huang, Y., Lu, D., Li, Y., & Zheng, Y. (2023) · Medical Physics · doi:10.1002/mp.16692 · Cited by 7 · see citing works

Training Automatic View Planner for Cardiac MR Imaging via Self-supervision by Spatial Relationship Between Views

Wei, D., Ma, K., & Zheng, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87231-1_51 · Cited by 7 · see citing works

Improved Post-hoc Probability Calibration for Out-of-Domain MRI Segmentation

Ouyang, C., Wang, S., Chen, C., Li, Z., Bai, W., Kainz, B., & Rueckert, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16749-2_6 · Cited by 8 · see citing works

Technical Report: Quality Assessment Tool for Machine Learning with Clinical CTPreprint

Gao, R., Khan, M. S., Tang, Y., Xu, K., Deppen, S., Huo, Y., Sandler, K. L., Massion, P. P., & Landman, B. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.12842 · Cited by 5 · see citing works

A Motion-Aware DNN Model with Edge Focus Loss and Quality Control for Short-Axis Left Ventricle Segmentation of Cine MR Sequences

Wang, Y., Sun, Z., Liu, Z., Lu, J., & Zhang, N. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-023-00942-6 · Cited by 3 · see citing works

Robust Image Segmentation Quality Assessment without Ground TruthPreprint

Zhou, L., Deng, W., & Wu, X. (2019) · arXiv (Cornell University) · Cited by 3 · see citing works

ConfIC-RCA: Statistically Grounded Efficient Estimation of Segmentation Quality

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Structure-guided registration in learning based image analysis

Lee, M. C. H. (2019) · Spiral (Imperial College London) · doi:10.25560/79296

Automatic quality control of corpus callosum segmentations in large population studies

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From out-of-distribution detection to quality control

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Improving the quality of information systems for diagnosis: an approach to detecting and correcting segmentation errors

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Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging StudyPreprint

Robinson, R., Valindria, V. V., Bai, W., Oktay, O., Kainz, B., Suzuki, H., Sanghvi, M. M., Aung, N., Paiva, J. M., Zemrak, F., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.09351 · Cited by 1 · see citing works

Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study

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Automatic initialization and quality control of large-scale cardiac MRI segmentations

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Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging

Hann, E., Biasiolli, L., Zhang, Q., Popescu, I. A., Werys, K., Lukaschuk, E. I., Carapella, V., Paiva, J. M., Aung, N., Rayner, J. J., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32245-8_83 · Cited by 29 · see citing works

Reliable machine learning for medical imaging data through automated quality control and data harmonization.

Robinson, R. H. (2020) · doi:10.25560/82259