EchoUNAL a benchmarking study for automatic classification of six echocardiographic views

@article{,
title= {EchoUNAL a benchmarking study for automatic classification of six echocardiographic views},
keywords= {echocardiographic view classification, cardiac ultrasound views, apical four chamber view, echocardiography CNN benchmarking},
author= {},
abstract= {# EchoUNAL Dataset & Classifier

This repository contains the dataset and resources for the paper, "The EchoCardiography open data base EchoUNAL: a benchmarking study for automatic classification of six echocardiographic views".

##  Description

The study presents a comparative evaluation of four CNN architectures for classifying six standard echocardiographic views:
* **A4C**: Apical Four Chamber 
* **A5C**: Apical Five Chamber 
* **PLAX**: Parasternal Long Axis 
* **PSAX**: Parasternal Short Axis 
* **S4C**: Subcostal Four Chambers
* **IVC**: Inferior Vena Cava 


##  Dataset Details

The database was created specifically for this study and is shared publicly to encourage further research.

* **Data Source**: The dataset was obtained from **89 healthy volunteers**, with informed consent from all participants.
* **Acquisition Hardware**: A **Butterfly iQ+** ultrasound device was used for image acquisition.
* **Acquisition Protocol**: Images were captured by two cardiologists specializing in echocardiography. Some videos were excluded due to poor acoustic window quality.
* **Total Contents**: The final dataset comprises **346 videos**, distributed as follows:
    * **A4C**: 61 videos 
    * **A5C**: 49 videos 
    * **PLAX**: 66 videos 
    * **PSAX**: 61 videos 
    * **S4C**: 54 videos 
    * **IVC**: 55 videos

##  Data Collection & Preprocessing

* **Original Format**: Videos were stored in `.AVI` format with a `500x500` pixel resolution. The original frame rate varied between 26 and 30 fps.
* **Applied Preprocessing**:
    1.  The ultrasound interface was cropped to center the cardiac image area.
    2.  The frame rate was standardized to **30 fps** using interpolation.
    3.  A manual, frame-level relabeling was performed to ensure label accuracy. A **"no view"** label was used for frames without a clear view, though this class was later excluded from the training setup due to its high heterogeneity.
    4.  All video frames were resized to **224x224** pixels, a standard input size for the pretrained CNN architectures.

##  Acknowledgments
This work was funded by the project "Estimation of cardiac work as an index of cardiovascular function in echocardiographic videos" with code 60946 from the call for research projects SUE Distrito Capital of 2023.






J. D. S. Avila et al., "The EchoCardiography open data base EchoUNAL: a benchmarking study for automatic classification of six echocardiographic views," 2025 21st International Symposium on Biomedical Image Processing and Analysis (SIPAIM), Pasto, Colombia, 2025, pp. 1-4, doi: 10.1109/SIPAIM67325.2025.11283340.
keywords: {Training;Visualization;Ultrasonic imaging;Systematics;Echocardiography;Computational modeling;Benchmark testing;Convolutional neural networks;Videos;Residual neural networks},},
terms= {},
license= {https://creativecommons.org/licenses/by-nc/4.0/},
superseded= {},
url= {https://gitlab.com/JSarmientoA/echoUNAL}
}