Estimation of Left and Right Ventricular Ejection Fractions from cine-MRI Using 3D-CNN Article Swipe
Soichiro Inomata
,
Takaaki Yoshimura
,
Minghui Tang
,
Shota Ichikawa
,
Hiroyuki Sugimori
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23146580
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23146580
Cardiac function indices must be calculated using tracing from short-axis images in cine-MRI. A 3D-CNN (convolutional neural network) that adds time series information to images can estimate cardiac function indices without tracing using images with known values and cardiac cycles as the input. Since the short-axis image depicts the left and right ventricles, it is unclear which motion feature is captured. This study aims to estimate the indices by learning the short-axis images and the known left and right ventricular ejection fractions and to confirm the accuracy and whether each index is captured as a feature. A total of 100 patients with publicly available short-axis cine images were used. The dataset was divided into training:test = 8:2, and a regression model was built by training with the 3D-ResNet50. Accuracy was assessed using a five-fold cross-validation. The correlation coefficient, MAE (mean absolute error), and RMSE (root mean squared error) were determined as indices of accuracy evaluation. The mean correlation coefficient of the left ventricular ejection fraction was 0.80, MAE was 9.41, and RMSE was 12.26. The mean correlation coefficient of the right ventricular ejection fraction was 0.56, MAE was 11.35, and RMSE was 14.95. The correlation coefficient was considerably higher for the left ventricular ejection fraction. Regression modeling using the 3D-CNN indicated that the left ventricular ejection fraction was estimated more accurately, and left ventricular systolic function was captured as a feature.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s23146580
- https://www.mdpi.com/1424-8220/23/14/6580/pdf?version=1689949509
- OA Status
- gold
- Cited By
- 11
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385213519
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4385213519Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s23146580Digital Object Identifier
- Title
-
Estimation of Left and Right Ventricular Ejection Fractions from cine-MRI Using 3D-CNNWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-21Full publication date if available
- Authors
-
Soichiro Inomata, Takaaki Yoshimura, Minghui Tang, Shota Ichikawa, Hiroyuki SugimoriList of authors in order
- Landing page
-
https://doi.org/10.3390/s23146580Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/23/14/6580/pdf?version=1689949509Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1424-8220/23/14/6580/pdf?version=1689949509Direct OA link when available
- Concepts
-
Ejection fraction, Mean squared error, Correlation coefficient, Mathematics, Artificial intelligence, Correlation, Pearson product-moment correlation coefficient, Linear regression, Coefficient of determination, Ventricular function, Cardiology, Medicine, Statistics, Computer science, Heart failure, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 4, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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