Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography
Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolu...
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| Veröffentlicht in: | IEEE transactions on medical imaging Jg. 38; H. 9; S. 2198 - 2210 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
IEEE
01.09.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers |
| Schlagworte: | |
| ISSN: | 0278-0062, 1558-254X, 1558-254X |
| Online-Zugang: | Volltext |
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| Abstract | Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e., segmenting cardiac structures and estimating clinical indices, on a dataset, especially, designed to answer this objective. We, therefore, introduce the cardiac acquisitions for multi-structure ultrasound segmentation dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder-based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6%. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer's ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images. |
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| AbstractList | Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e., segmenting cardiac structures and estimating clinical indices, on a dataset, especially, designed to answer this objective. We, therefore, introduce the cardiac acquisitions for multi-structure ultrasound segmentation dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder-based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6%. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer’s ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images. Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e., segmenting cardiac structures and estimating clinical indices, on a dataset, especially, designed to answer this objective. We, therefore, introduce the cardiac acquisitions for multi-structure ultrasound segmentation dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder-based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6%. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer's ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images.Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e., segmenting cardiac structures and estimating clinical indices, on a dataset, especially, designed to answer this objective. We, therefore, introduce the cardiac acquisitions for multi-structure ultrasound segmentation dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder-based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6%. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer's ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images. Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e. segmenting cardiac structures as well as estimating clinical indices, on a dataset especially designed to answer this objective. We therefore introduce the Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CA-MUS) dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and endsystolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6%. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer's ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images. |
| Author | Espinosa, Florian Ostvik, Andreas Berg, Erik Andreas Rye Leclerc, Sarah Lartizien, Carole Jodoin, Pierre-Marc Cervenansky, Frederic Espeland, Torvald Smistad, Erik Lovstakken, Lasse Bernard, Olivier Dhooge, Jan Pedrosa, Joao Grenier, Thomas |
| Author_xml | – sequence: 1 givenname: Sarah orcidid: 0000-0002-4271-0292 surname: Leclerc fullname: Leclerc, Sarah email: sarah.leclerc@gmx.fr organization: University of Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, University of Lyon 1, Villeurbanne, France – sequence: 2 givenname: Erik surname: Smistad fullname: Smistad, Erik organization: Department of Circulation and Medical Imaging, Center of Innovative Ultrasound Solutions, Norwegian University of Science and Technology, Trondheim, Norway – sequence: 3 givenname: Joao orcidid: 0000-0001-9926-0774 surname: Pedrosa fullname: Pedrosa, Joao organization: Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium – sequence: 4 givenname: Andreas orcidid: 0000-0003-3895-2683 surname: Ostvik fullname: Ostvik, Andreas organization: Department of Circulation and Medical Imaging, Center of Innovative Ultrasound Solutions, Norwegian University of Science and Technology, Trondheim, Norway – sequence: 5 givenname: Frederic surname: Cervenansky fullname: Cervenansky, Frederic organization: University of Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, University of Lyon 1, Villeurbanne, France – sequence: 6 givenname: Florian surname: Espinosa fullname: Espinosa, Florian organization: Cardiovascular Department, Centre Hospitalier Universitaire de Saint-Etienne, Saint-Etienne, France – sequence: 7 givenname: Torvald surname: Espeland fullname: Espeland, Torvald organization: Center of Innovative Ultrasound Solutions and the Clinic of Cardiology, St. Olavs Hospital, Trondheim, Norway – sequence: 8 givenname: Erik Andreas Rye surname: Berg fullname: Berg, Erik Andreas Rye organization: Center of Innovative Ultrasound Solutions and the Clinic of Cardiology, St. Olavs Hospital, Trondheim, Norway – sequence: 9 givenname: Pierre-Marc orcidid: 0000-0002-6038-5753 surname: Jodoin fullname: Jodoin, Pierre-Marc organization: Computer Science Department, University of Sherbrooke, Sherbrooke, QC, Canada – sequence: 10 givenname: Thomas surname: Grenier fullname: Grenier, Thomas organization: University of Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, University of Lyon 1, Villeurbanne, France – sequence: 11 givenname: Carole surname: Lartizien fullname: Lartizien, Carole organization: University of Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, University of Lyon 1, Villeurbanne, France – sequence: 12 givenname: Jan orcidid: 0000-0002-2346-142X surname: Dhooge fullname: Dhooge, Jan organization: Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium – sequence: 13 givenname: Lasse surname: Lovstakken fullname: Lovstakken, Lasse organization: Department of Circulation and Medical Imaging, Center of Innovative Ultrasound Solutions, Norwegian University of Science and Technology, Trondheim, Norway – sequence: 14 givenname: Olivier orcidid: 0000-0003-0752-9946 surname: Bernard fullname: Bernard, Olivier email: olivier.bernard@creatis.insa-lyon.fr organization: University of Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, University of Lyon 1, Villeurbanne, France |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30802851$$D View this record in MEDLINE/PubMed https://hal.science/hal-02054458$$DView record in HAL |
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| References | keraudren (ref15) 2014 ref30 ref2 ref1 ref19 domingos (ref16) 2014 çiçek (ref20) 2016 smistad (ref10) 2014 ref24 oktay (ref14) 2014 ref23 smistad (ref17) 2017 ref25 ref22 van stralen (ref13) 2014 ref21 ref28 ref27 ref29 wang (ref9) 2014 ref8 bernier (ref11) 2014 ref7 ronneberger (ref18) 2015 ref3 ref6 ref5 barbosa (ref4) 2014 leclerc (ref26) 2017 milletari (ref12) 2014 |
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| SubjectTerms | Algorithms Artificial neural networks Automation Cardiacsegmentation and diagnosis Coders Correlation coefficient Correlation coefficients Databases, Factual Datasets Deep Learning Echocardiography Echocardiography - methods Encoders-Decoders Engineering Sciences Heart Heart - diagnostic imaging Humans Image processing Image Processing, Computer-Assisted - methods Image segmentation left atrium left ventricle Machine learning Medical diagnosis Medical imaging Myocardium Neural networks Signal and Image processing Three-dimensional displays Training Two dimensional analysis Two dimensional displays Ultrasonic imaging Ultrasound Ventricle |
| Title | Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography |
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