ML Anthology

RVENet: A Large Echocardiographic Dataset for the Deep Learning-Based Assessment of Right Ventricular Function

Bálint Magyar, Márton Tokodi, András Soós, Máté Tolvaj, Bálint Károly Lakatos, Alexandra Fábián, Elena Surkova, Béla Merkely, Attila Kovács, András Horváth
ECCVW 2022 pp. 569-583
doi:10.1007/978-3-031-25066-8_33 /eccvw/2022/magyar2022eccvw-rvenet/

Abstract

Right ventricular ejection fraction (RVEF) is an important indicator of cardiac function and has a well-established prognostic value. In scenarios where imaging modalities capable of directly assessing RVEF are unavailable, deep learning (DL) might be used to infer RVEF from alternative modalities, such as two-dimensional echocardiography. For the implementation of such solutions, publicly available, dedicated datasets are pivotal. Accordingly, we introduce the RVENet dataset comprising 3,583 two-dimensional apical four-chamber view echocardiographic videos of 831 patients. The ground truth RVEF values were calculated by medical experts using three-dimensional echocardiography. We also implemented benchmark DL models for two tasks: (i) the classification of RVEF as normal or reduced and (ii) the prediction of the exact RVEF values. In the classification task, the DL models were able to surpass the medical experts’ performance. We hope that the publication of this dataset may foster innovations targeting the accurate diagnosis of RV dysfunction.

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Cite

Text

Magyar et al. "RVENet: A Large Echocardiographic Dataset for the Deep Learning-Based Assessment of Right Ventricular Function." European Conference on Computer Vision Workshops, 2022. doi:10.1007/978-3-031-25066-8_33

Markdown

[Magyar et al. "RVENet: A Large Echocardiographic Dataset for the Deep Learning-Based Assessment of Right Ventricular Function." European Conference on Computer Vision Workshops, 2022.](https://mlanthology.org/eccvw/2022/magyar2022eccvw-rvenet/) doi:10.1007/978-3-031-25066-8_33

BibTeX

@inproceedings{magyar2022eccvw-rvenet,
  title     = {{RVENet: A Large Echocardiographic Dataset for the Deep Learning-Based Assessment of Right Ventricular Function}},
  author    = {Magyar, Bálint and Tokodi, Márton and Soós, András and Tolvaj, Máté and Lakatos, Bálint Károly and Fábián, Alexandra and Surkova, Elena and Merkely, Béla and Kovács, Attila and Horváth, András},
  booktitle = {European Conference on Computer Vision Workshops},
  year      = {2022},
  pages     = {569-583},
  doi       = {10.1007/978-3-031-25066-8_33},
  url       = {https://mlanthology.org/eccvw/2022/magyar2022eccvw-rvenet/}
}