Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?
Open accessAbstract
Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.
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The ACDC dataset contains two labelled 3D cardiac scans from 100 patients.
“This dataset contains two labelled 3D cardiac scans from 100 patients.”
From rNCA: Self-Repairing Segmentation Masks (2025) · cites this paper as [47]
The Evaluation Framework (section III, page unavailable) says that the 100-patient training set provides manual references for end-diastolic and end-systolic images. It also says ACDC includes 50 test patients, whose manual references are kept private. The claim describes the labelled training set but omits that qualification, making 100 sound like the size of the full dataset. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe ACDC dataset was used for myocardium segmentation.
“For myocardium segmentation, we used the ACDC dataset [47].”
From rNCA: Self-Repairing Segmentation Masks (2025) · cites this paper as [47]
Zhou et al. presented U-Net++, a new, broader neural net topology for image processing, to take categorization further.
“Zhou et al. presented U-Net ++ , a new, broader neural net topology for image processing, to take categorization further and farther [68], [70], [71].”
From A Review of Trustworthy and Explainable Artificial Intelligence (XAI) (2023) · cites this paper as [68]
The Evaluated Architectures section discusses U-Net and several variants, but does not mention Zhou et al. or U-Net++. Searches for both names found no passage describing the claimed topology or its contribution to classification. No page number was available in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe training dataset of SAM, SA-1B, contains 11 million photos, including natural locations, objects, and scenes, but without any medical images.
“The training dataset of SAM, i.e., SA-1B 5 contains 11 million photos, including natural locations, objects, and scenes, but without any medical images.”
From Segment anything model for medical images? (2024) · cites this paper as [5]
ACDC is a large publicly available and fully annotated dataset for cardiac MRI assessment, consisting of 150 patients divided into 5 categories.
“Automatic Cardiac Diagnosis Challenge (ACDC) [111] is a large publicly available and fully annotated dataset for cardiac MRI assessment.”
From Federated learning for medical image analysis: A survey (2024) · cites this paper as [111]
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Cites
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 · see citing works
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 · see citing works
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 · see citing works
Learning Spatiotemporal Features with 3D Convolutional Networks
Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.510 · Cited by 9,593 · see citing works
A formula to estimate the approximate surface area if height and weight be known. 1916.
Bois, D. D., & Bois, E. F. D. (1989) · PubMed · Cited by 5,303 · see citing works
2014 ESC Guidelines on diagnosis and management of hypertrophic cardiomyopathy
Authors/Task Force members, Elliott, P. M., Anastasakis, A., Borger, M. A., Borggrefe, M., Cecchi, F., Charron, P., Hagege, A. A., Lafont, A., Limongelli, G., et al. (2014) · European Heart Journal · doi:10.1093/eurheartj/ehu284 · Cited by 4,332 · see citing works
Marcus, F. I., McKenna, W. J., Sherrill, D., Basso, C., Bauce, B., Bluemke, D. A., Calkins, H., Corrado, D., Cox, M. G. P. J., Daubert, J. P., et al. (2010) · Circulation · doi:10.1161/circulationaha.108.840827 · Cited by 2,676 · see citing works
White, H. D., Norris, R. M., Brown, M. A., Brandt, P. W., Whitlock, R. M. L., & Wild, C. J. (1987) · Circulation · doi:10.1161/01.cir.76.1.44 · Cited by 2,357 · see citing works
OBBTree: a hierarchical structure for rapid interference detection
Gottschalk, S., Lin, M. C., & Manocha, D. (1996) · Proceedings of the 23rd annual conference on Computer graphics and interactive techniques · doi:10.1145/237170.237244 · Cited by 2,081 · see citing works
Warfield, S. K., Zou, K. H., & Wells, W. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.828354 · Cited by 2,072 · see citing works
Marcus, G. M., McKenna, W. J., Sherrill, D., Basso, C., Bauce, B., Bluemke, D. A., Calkins, H. G., Corrado, D., Cox, M. G. P. J., Daubert, J. P., et al. (2010) · European Heart Journal · doi:10.1093/eurheartj/ehq025 · Cited by 1,610 · see citing works
A review of segmentation methods in short axis cardiac MR images
Petitjean, C., & Dacher, J. (2010) · Medical Image Analysis · doi:10.1016/j.media.2010.12.004 · Cited by 732 · see citing works
Lorenz, C., Walker, E., Morgan, V., Klein, S., & Graham, T. (1999) · Journal of Cardiovascular Magnetic Resonance · doi:10.3109/10976649909080829 · Cited by 717 · see citing works
Principles and applications of balanced SSFP techniques
Scheffler, K., & Lehnhardt, S. (2003) · European Radiology · doi:10.1007/s00330-003-1957-x · Cited by 714 · see citing works
Avendi, M. R., Kheradvar, A., & Jafarkhani, H. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.01.005 · Cited by 591 · see citing works
UK Biobank's cardiovascular magnetic resonance protocol
Petersen, S. E., Matthews, P. M., Francis, J. M., Robson, M. D., Zemrak, F., Boubertakh, R., Young, A. A., Hudson, S., Weale, P., Garratt, S., et al. (2016) · Journal of Cardiovascular Magnetic Resonance · doi:10.1186/s12968-016-0227-4 · Cited by 481 · see citing works
Self-citation3-D active appearance models: segmentation of cardiac MR and ultrasound images
Mitchell, S., Bosch, J. G., Lelieveldt, B. P. F., van der Geest, R. J., Reiber, J. H. C., & Milan Sonka, A. (2002) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2002.804425 · Cited by 392 · see citing works
Evaluation Framework for Algorithms Segmenting Short Axis Cardiac MRI.
Radau, P. E., Lu, Y., Connelly, K. A., Paul, G. A., Dick, A. J., & Wright, G. A. (2009) · The MIDAS Journal · doi:10.54294/g80ruo · Cited by 373 · see citing works
Ngo, T. A., Lu, Z., & Carneiro, G. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.05.009 · Cited by 334 · see citing works
Svf-net: learning deformable image registration using shape matching
Rohé, M.-M., Datar, M., Heimann, T., Sermesant, M., & Pennec, X. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66182-7_31 · Cited by 330 · cited 1× · see citing works
“The registration module implements an encoder-decoder network called SVFNet [56].”
MethodSelf-citationA Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI
Tran, P. V. (2016) · arXiv · doi:10.48550/arxiv.1604.00494 · Cited by 323 · see citing works
Isensee, F., Jaeger, P. F., Full, P. M., Ivo Wolf, A., Engelhardt, S., & Maier-Hein, K. H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-75541-0_13 · Cited by 313 · see citing works
Self-citationRecurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation
Poudel, R. P. K., Lamata, P., & Montana, G. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-52280-7_8 · Cited by 258 · see citing works
Multi-input Cardiac Image Super-Resolution Using Convolutional Neural Networks
Oktay, O., Bai, W., Lee, M., Guerrero, R., Kamnitsas, K., Caballero, J., de Marvao, A., Cook, S., O’Regan, D., & Rueckert, D. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46726-9_29 · Cited by 246 · see citing works
Right ventricle segmentation from cardiac MRI: A collation study
Petitjean, C., Zuluaga, M. A., Bai, W., Dacher, J., Grosgeorge, D., Caudron, J., Ruan, S., Ayed, I. B., Cardoso, M. J., Chen, H.-C., et al. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.10.004 · Cited by 242 · see citing works
An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation
Baumgartner, C. F., Koch, L. M., Pollefeys, M., & Ender Konukoglu, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-75541-0_12 · Cited by 234 · see citing works
Self-citationSuinesiaputra, A., Cowan, B. R., Al‐Agamy, A. O., Elattar, M., Ayache, N., Fahmy, A. S., Khalifa, A. M., Medrano−Gracia, P., Jolly, M., Kadish, A. H., et al. (2013) · Medical Image Analysis · doi:10.1016/j.media.2013.09.001 · Cited by 206 · see citing works
M-net: A Convolutional Neural Network for deep brain structure segmentation
Mehta, R., & Sivaswamy, J. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950555 · Cited by 171 · see citing works
Multi-atlas segmentation with augmented features for cardiac MR images
Bai, W., Shi, W., Ledig, C., & Rueckert, D. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.09.005 · Cited by 159 · see citing works
Fast automatic myocardial segmentation in 4D cine CMR datasets
Queirós, S., Barbosa, D. C., Heyde, B., Morais, P., Vilaça, J. L., Friboulet, D. P., Bernard, O., & D’hooge, J. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.06.001 · Cited by 146 · see citing works
Self-citationAutomatic Segmentation and Disease Classification Using Cardiac Cine MR Images
Wolterink, J. M., Leiner, T., Viergever, M. A., & Išgum, I. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-75541-0_11 · Cited by 144 · see citing works
Self-citation2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation
Patravali, J., Jain, S., & Chilamkurthy, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-75541-0_14 · Cited by 130 · see citing works
Self-citationRecognizing End-Diastole and End-Systole Frames via Deep Temporal Regression Network
Kong, B., Zhan, Y., Shin, M. C., Denny, T., & Zhang, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46726-9_31 · Cited by 117 · see citing works
GridNet with Automatic Shape Prior Registration for Automatic MRI Cardiac Segmentation
Zotti, C., Luo, Z., Humbert, O., Lalande, A., & Jodoin, P. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-75541-0_8 · Cited by 116 · see citing works
Self-citationKhened, M., Alex, V., & Krishnamurthi, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-75541-0_15 · Cited by 111 · see citing works
Self-citationGraph cut segmentation with a statistical shape model in cardiac MRI
Grosgeorge, D., Petitjean, C., Dacher, J.-N., & Ruan, S. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.01.014 · Cited by 94 · see citing works
Automatic Segmentation of LV and RV in Cardiac MRI
Jang, Y., Hong, Y., Ha, S., Kim, S., & Chang, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-75541-0_17 · Cited by 83 · see citing works
Self-citationAutomatic localization of the left ventricle in cardiac MRI images using deep learning
Emad, O., Yassine, I. A., & Fahmy, A. S. (2015) · doi:10.1109/embc.2015.7318454 · Cited by 80 · see citing works
Ayed, I. B., Chen, H.-M., Punithakumar, K., Ross, I., & Li, S. (2011) · Medical Image Analysis · doi:10.1016/j.media.2011.05.009 · Cited by 79 · see citing works
Chen, T., Babb, J. S., Kellman, P., Axel, L., & Kim, D. C. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.918327 · Cited by 69 · see citing works
Liu, H., Hu, H., Xu, X., et al. (2012) · Academic Radiology · doi:10.1016/j.acra.2012.02.011 · Cited by 68 · see citing works
In-line Automated Tracking for Ventricular Function With Magnetic Resonance Imaging
Li, B., Liu, Y., Occleshaw, C. J., Cowan, B. R., & Young, A. A. (2010) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2010.04.013 · Cited by 66 · see citing works
Miller, C., Jordan, P., Borg, A., Argyle, R., Clark, D., Pearce, K., & Schmitt, M. (2012) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.23892 · Cited by 66 · see citing works
Class-Balanced Deep Neural Network for Automatic Ventricular Structure Segmentation
Yang, X., Bian, C., Yu, L., Ni, D., & Heng, P. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-75541-0_16 · Cited by 64 · see citing works
Self-citationDeep Active ContoursPreprint
Rupprecht, C., Huaroc, E., Baust, M., & Navab, N. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.05074 · Cited by 53 · see citing works
Automatic Multi-Atlas Segmentation of Myocardium with SVF-Net
Rohé, M.-M., Maxime Sermesant, B., & Pennec, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-75541-0_18 · Cited by 52 · see citing works
Self-citationLeft Ventricular Segmentation Challenge from Cardiac MRI: A Collation Study
Suinesiaputra, A., Cowan, B. R., Finn, J. P., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-28326-0_9 · Cited by 51 · see citing works
Automated Quality Assessment of Cardiac MR Images Using Convolutional Neural Networks
Zhang, L., Gooya, A., Dong, B., Hua, R., Petersen, S. E., Medrano−Gracia, P., & Frangi, A. F. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46630-9_14 · Cited by 50 · see citing works
Self-citationGrinias, E., & Tziritas, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-75541-0_10 · Cited by 50 · see citing works
Self-citationDeep Fusion Net for Multi-atlas Segmentation: Application to Cardiac MR Images
Yang, H., Sun, J., Li, H., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46723-8_60 · Cited by 38 · see citing works
Norris, R. M., White, H. D., Cross, D. B., et al. (1992) · European Heart Journal · doi:10.1093/oxfordjournals.eurheartj.a060113 · Cited by 37 · see citing works
An Efficient Optimization Framework for Multi-Region Segmentation Based on Lagrangian Duality
Ulén, J., Strandmark, P., & Kahl, F. (2012) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2012.2218117 · Cited by 33 · see citing works
Human-level CMR image analysis with deep fully convolutional networks.Preprint
Bai, W., Sinclair, M. D., Tarroni, G., et al. (2017) · arXiv (Cornell University) · Cited by 33 · see citing works
Cardiac left ventricle segmentation using convolutional neural network regression
Tan, L. K., Liew, Y. M., Lim, E., et al. (2016) · IEEE-EMBS Conference on Biomedical Engineering and Sciences · doi:10.1109/iecbes.2016.7843499 · Cited by 30 · see citing works
Evaluation of Cardiac Structure Segmentation in Cine Magnetic Resonance Imaging
Lalande, A., Garreau, M., Frouin, F., & GRIC‐MediEval (2015) · Wiley eBooks · doi:10.1002/9781118574362.ch5 · Cited by 12 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 4
- M. Havaei, N. Guizard, H. Larochelle, and P.-M. Jodoin, "Deep learning trends for focal brain pathology segmentation in mri," in book Machine Learning for Health Informatics, State-of-the-art and future challenges, 2015, pp. 125-148, LNAI 9605, Springer.
- F. I. Marcus, W. J. McKenna, D. Sherrill, C. Basso, B. Bauce, D. A. Bluemke, H. Calkins, D. Corrado, M. G. Cox et al., "Diagnosis of ar- rhythmogenic right ventricular cardiomyopathy / dysplasia," Circulation, vol. 121, no. 13, pp. 1533-1541, 2010.
- I. Cetin, G. Sanroma, S. E. Petersen, S. Napel, O. Camara, M. ngel Gonzlez Ballester, and K. Lekadir, "A radiomics approach to computer- aided diagnosis in cardiac cine-mri," in Proc. STACOM-MICCAI, LNCS, volume 10663, 2017, pp. 82-90.
- J. Bogaert, S. Dymarkowski, A. Taylor, and V. Muthurangu, "Cardiac Function," in Clinical Cardiac MRI. Springer, 2012, pp. 109-168.
Cited by
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nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021) · Nature Methods · doi:10.1038/s41592-020-01008-z · Cited by 10,477 · see citing works
Self-citationLoss odyssey in medical image segmentation
Ma, J., Chen, J., Ng, M., Huang, R., Li, Y., Li, C., Yang, X., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102035 · Cited by 662 · see citing works
Self-citationSegment anything model for medical images?
Huang, Y., Yang, X., Liu, L., Zhou, H., Chang, A., Zhou, X., Chen, R., Yu, J., Chen, J., Chen, C., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2023.103061 · Cited by 536 · see citing works
Self-citationFederated learning for medical image analysis: A survey
Guan, H., Yap, P.-T., Bozoki, A., & Liu, M. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110424 · Cited by 518 · see citing works
Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge
Campello, V. M., Gkontra, P., Izquierdo, C., Martin-Isla, C., Sojoudi, A., Full, P. M., Maier-Hein, K., Zhang, Y., He, Z., Ma, J., et al. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3090082 · Cited by 481 · see citing works
Self-citationA Review of Trustworthy and Explainable Artificial Intelligence (XAI)
Chamola, V., Hassija, V., Sulthana, A. R., Ghosh, D., Dhingra, D., & Sikdar, B. (2023) · IEEE Access · doi:10.1109/access.2023.3294569 · Cited by 352 · see citing works
A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
Xu, X., Li, J., Zhu, Z., Zhao, L., Wang, H., Song, C., Chen, Y., Zhao, Q., Yang, J., & Pei, Y. (2024) · Bioengineering · doi:10.3390/bioengineering11030219 · Cited by 242 · see citing works
Duan, J., Bello, G., Schlemper, J., Bai, W., Dawes, T. J. W., Biffi, C., de Marvao, A., Doumoud, G., O'Regan, D. P., & Rueckert, D. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2894322 · Cited by 227 · see citing works
Linardos, A., Kushibar, K., Walsh, S., Gkontra, P., & Lekadir, K. (2022) · Scientific Reports · doi:10.1038/s41598-022-07186-4 · Cited by 130 · see citing works
Self-citationA systematic review on federated learning in medical image analysis
Sohan, M. F., & Basalamah, A. (2023) · IEEE Access · doi:10.1109/access.2023.3260027 · Cited by 114 · see citing works
Reviewing Federated Machine Learning and Its Use in Diseases Prediction
Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Sensors · doi:10.3390/s23042112 · Cited by 114 · see citing works
Applications of Artificial Intelligence in Cardiology. The Future is Already Here
Dorado-Díaz, P. I., Sampedro-Gómez, J., Vicente-Palacios, V., & Sánchez, P. L. (2019) · Revista Española de Cardiología (English Edition) · doi:10.1016/j.rec.2019.05.014 · Cited by 111 · see citing works
Puyol-Antón, E., Ruijsink, B., Mariscal Harana, J., Piechnik, S. K., Neubauer, S., Petersen, S. E., Razavi, R., Chowienczyk, P., & King, A. P. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.859310 · Cited by 85 · see citing works
Self-citationA co-design framework of neural networks and quantum circuits towards quantum advantage
Jiang, W., Xiong, J., & Shi, Y. (2021) · Nature Communications · doi:10.1038/s41467-020-20729-5 · Cited by 63 · see citing works
Luo, G., Dong, S., Wang, W., Wang, K., Cao, S., Tam, C., Zhang, H., Howey, J., Ohorodnyk, P., & Li, S. (2020) · Medical Image Analysis · doi:10.1016/j.media.2019.101591 · Cited by 30 · see citing works
Wang, S., Zhou, X., Li, C., Wang, S., Li, Y., Tan, T., & Zheng, H. (2025) · Research · doi:10.34133/research.1029 · Cited by 29 · see citing works
Retrieval-Augmented Few-Shot Medical Image Segmentation With Foundation Models
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Have You Forgotten? A Method to Assess if Machine Learning Models Have Forgotten Data
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Efficient Model Monitoring for Quality Control in Cardiac Image Segmentation
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Anatomically plausible segmentations: Explicitly preserving topology through prior deformations
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Self-citationLiu, S., Bao, L., Yang, Q., Geng, W., Zheng, B., Li, C., Chen, W., Peng, H., & Yuan, Y. (2026) · arXiv · doi:10.48550/arxiv.2602.03320 · Cited by 1 · see citing works
“…relative quality of the rollouts O i : Land-man et al., 2015; Ji et al., 2022; Ma et al., 2022; Heller et al., 2023; Armato III et al., 2011), MRI (Bernard et al., 2018; Ji et al., 2022; Buda et al., 2019), X-Ray (Jaeger et al., 2014; Chowdhury et al., 2020; Khaled et al., 2021), Ultrasound…”
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- Authors
- Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.-A., Cetin, I., Lekadir, K., Camara, O., Gonzalez Ballester, M. A., Sanroma, G., Napel, S., Petersen, S., Tziritas, G., Grinias, E., Khened, M., Kollerathu, V. A., Krishnamurthi, G., Rohé, M.-M., Pennec, X., Sermesant, M., Isensee, F., Jäger, P., Maier-Hein, K. H., Full, P. M., Wolf, I., Engelhardt, S., Baumgartner, C. F., Koch, L. M., Wolterink, J. M., Išgum, I., Jang, Y., Hong, Y., Patravali, J., Jain, S., Humbert, O., & Jodoin, P.-M.
- Affiliations
- Lyon 1 Université (FR)
- Centre National de la Recherche Scientifique (FR)
- Computer Science Department, University of Sherbrooke, Sherbrooke, Canada
- University of Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, University of Lyon 1, Lyon, France
- Chinese University of Hong Kong (HK)
- Universitat Pompeu Fabra (ES)
- Barcelona Centre for New Medical Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Stanford Medicine (US)
- Queen Mary University of London (GB)
- University of Crete (GR)
- Indian Institute of Technology Madras (IN)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Inria-Asclepios Project, Sophia Antipolis, France
- German Cancer Research Center (DE)
- University Hospital Heidelberg (DE)
- Department of Computer Science, Mannheim University of Applied Sciences, Mannheim, Germany
- Technische Hochschule Mannheim (DE)
- ETH Zurich (CH)
- University Medical Center Utrecht (NL)
- Yonsei University (KR)
- Qure.ai Technologies Private Limited (India) (IN)
- PHEN X - Physiopathologie, Environnement et Exposome (FR)
- Université de Sherbrooke (CA)
- ORCID
- Bernard, Olivier
- Lalande, Alain
- Zotti, Clement
- Yang, Xin
- Heng, Pheng-Ann
- Cetin, Irem
- Lekadir, Karim
- Napel, Sandy
- Petersen, Steffen
- Tziritas, Georgios
- Grinias, Elias
- Khened, Mahendra
- Kollerathu, Varghese Alex
- Krishnamurthi, Ganapathy
- Pennec, Xavier
- Sermesant, Maxime
- Isensee, Fabian
- Jäger, Paul
- Maier-Hein, Klaus H.
- Full, Peter M.
- Engelhardt, Sandy
- Baumgartner, Christian F.
- Koch, Lisa M.
- Wolterink, Jelmer M.
- Išgum, Ivana
- Jang, Yeonggul
- Hong, Yoonmi
- Jain, Shubham
- Humbert, Olivier
- Jodoin, Pierre-Marc
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2018-11
- Volume
- 37
- Issue
- 11
- Pages
- 2514-2525
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2804047627
- Cited by
- 2,431
- References
- 55
- Citations by year
- 2026: 436 · 2025: 557 · 2024: 415 · 2023: 308 · 2022: 271 · 2021: 205 · 2020: 146 · 2019: 80 · 2018: 12 · 2017: 1
- FWCI
- 55.40
- Type
- journal article
- Language
- en
- Open access
- green
- qmro.qmul.ac.uk
- Licence
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
- HAL (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion)
- Funders
- British Heart Foundation (PG/14/89/31194)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- cardiac MRI, myocardium segmentation, left ventricle segmentation, right ventricle segmentation, ACDC dataset, cine MRI, clinical indicators, deep learning segmentation
- MeSH terms
- Deep Learning, Female, Heart, Heart Diseases, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Databases, Factual, Cardiac Imaging Techniques
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.
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