Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography
Université Claude Bernard Lyon 1 · Centre National de la Recherche Scientifique · +7 more institutions
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.…
Citation impact
- FWCI
- 30.40
- Percentile
- 100%
- References
- 30
Authors
14- SLSarah LeclercCorresponding
Université Claude Bernard Lyon 1, Centre National de la Recherche Scientifique, Inserm, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
- ESErik Smistad
Norwegian University of Science and Technology
- JPJoao Pedrosa
KU Leuven
- AOAndreas Ostvik
- FCFrederic Cervenansky
Université Claude Bernard Lyon 1, Centre National de la Recherche Scientifique, Inserm, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
Topics & keywords
- Deep learning
- Segmentation
- Convolutional neural network
- Mean absolute error
- Pattern recognition (psychology)
- Artificial neural network
- Correlation coefficient
- Ejection fraction