Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?
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 decade...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
October 29, 2018
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| In: |
IEEE transactions on medical imaging
Year: 2018, Jahrgang: 37, Heft: 11, Pages: 2514-2525 |
| ISSN: | 1558-254X |
| DOI: | 10.1109/TMI.2018.2837502 |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1109/TMI.2018.2837502
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| Verfasserangaben: | Olivier Bernard, Alain Lalande, Clement Zotti, Frederick Cervenansky, Xin Yang, Pheng-Ann Heng, Irem Cetin, Karim Lekadir, Oscar Camara, Miguel Angel Gonzalez Ballester, Gerard Sanroma, Sandy Napel, Steffen Petersen, Georgios Tziritas, Elias Grinias, Mahendra Khened, Varghese Alex Kollerathu, Ganapathy Krishnamurthi, Marc-Michel Rohé, Xavier Pennec, Maxime Sermesant, Fabian Isensee, Paul Jäger, Klaus H. Maier-Hein, Peter M. Full, Ivo Wolf, Sandy Engelhardt, Christian F. Baumgartner, Lisa M. Koch, Jelmer M. Wolterink, Ivana Išgum, Yeonggul Jang, Yoonmi Hong, Jay Patravali, Shubham Jain, Olivier Humbert, and Pierre-Marc Jodoin |
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| 245 | 1 | 0 | |a Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis |b is the problem solved? |c Olivier Bernard, Alain Lalande, Clement Zotti, Frederick Cervenansky, Xin Yang, Pheng-Ann Heng, Irem Cetin, Karim Lekadir, Oscar Camara, Miguel Angel Gonzalez Ballester, Gerard Sanroma, Sandy Napel, Steffen Petersen, Georgios Tziritas, Elias Grinias, Mahendra Khened, Varghese Alex Kollerathu, Ganapathy Krishnamurthi, Marc-Michel Rohé, Xavier Pennec, Maxime Sermesant, Fabian Isensee, Paul Jäger, Klaus H. Maier-Hein, Peter M. Full, Ivo Wolf, Sandy Engelhardt, Christian F. Baumgartner, Lisa M. Koch, Jelmer M. Wolterink, Ivana Išgum, Yeonggul Jang, Yoonmi Hong, Jay Patravali, Shubham Jain, Olivier Humbert, and Pierre-Marc Jodoin |
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| 520 | |a 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. | ||
| 650 | 4 | |a 150 multiequipments | |
| 650 | 4 | |a 2017 MICCAI-ACDC challenge | |
| 650 | 4 | |a Automatic Cardiac Diagnosis Challenge dataset | |
| 650 | 4 | |a automatic diagnosis | |
| 650 | 4 | |a automatic extraction | |
| 650 | 4 | |a automatic MRI cardiac multistructures segmentation | |
| 650 | 4 | |a Biomedical imaging | |
| 650 | 4 | |a biomedical MRI | |
| 650 | 4 | |a cardiac CMRI | |
| 650 | 4 | |a cardiac magnetic resonance images | |
| 650 | 4 | |a cardiac MRI assessment | |
| 650 | 4 | |a Cardiac segmentation and diagnosis | |
| 650 | 4 | |a cardiology | |
| 650 | 4 | |a classification task | |
| 650 | 4 | |a common clinical task | |
| 650 | 4 | |a corresponding tasks | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a deep learning techniques | |
| 650 | 4 | |a fully annotated dataset | |
| 650 | 4 | |a fully automatic analysis | |
| 650 | 4 | |a Heart | |
| 650 | 4 | |a highly accurate analysis | |
| 650 | 4 | |a image segmentation | |
| 650 | 4 | |a Image segmentation | |
| 650 | 4 | |a intense research | |
| 650 | 4 | |a largest publicly available annotated dataset | |
| 650 | 4 | |a learning (artificial intelligence) | |
| 650 | 4 | |a left and right ventricles | |
| 650 | 4 | |a left ventricular cavity | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Magnetic resonance imaging | |
| 650 | 4 | |a medical experts | |
| 650 | 4 | |a medical image processing | |
| 650 | 4 | |a MRI | |
| 650 | 4 | |a multislice 2-D cine MRI | |
| 650 | 4 | |a myocardium | |
| 650 | 4 | |a Myocardium | |
| 650 | 4 | |a reference measurements | |
| 650 | 4 | |a segmentation task | |
| 650 | 4 | |a state-of-the-art deep learning methods | |
| 650 | 4 | |a Task analysis | |
| 650 | 4 | |a ventricle | |
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