# Introduction
Cardiac ejection fraction (EF) is a measurement, expressed as a percentage, of how much blood the left ventricle pumps out with each contraction [1]. Low cardiac EF might be a sign of heart failure, and therefore have clinical relevance. Cardiac EF is usually measured in the left ventricle, which is the heart's main pumping chamber. The left chamber pumps oxygen-rich blood up into your body's main artery (aorta) to the rest of the body [2]. 

Today cardiovascular MRI (CMR) is the gold standard for the calculation of left ventricular ejection fraction (LVEF)[3]. However, each cardiac MRI scan can cost between $1000-5000 or on average 5.5-times the price of echocardiography [4]. It would therefore have great health economical benefits if echocardiograms could be used instead of CMR to determine the left ventricular ejection fraction.

## The challenge
AI has shown great potential in medical image classification and its reason to believe that it can be beneficial in terms of interpreting echocardiograms. In this dataset, we present echo loops, or sequences of echocardiograms. In the training set, there are images from 400 patients and in the test set, there are 50 patients. The objective is to predict the cardiac EF of the 50 patients in the test set.

#Data
The data set contains both 2 channel (2CH) echo images and 4 channel (4CH) echo images. 2CH images show only the left ventricle and the left atrium, while 4CH shows both the left and right atrium and ventricle. The competitors are free to choose whether they want to use only 4CH, only 2CH, or both in their training and prediction. 

The image data are stored in .npy files. These files can be loaded into Python using `numpy.load`
```
import numpy as np

np.load("./my_path/my_echofile.npy")
```
Each .npy-file contains an entire echo loop. All images inside an echo loop have the same resolution and image size, but the image size varies from patient to patient. 

The ejection fraction (ground truth) for the training data is given inside the file called **train_data.csv**.  This file can be uploaded to Python using `pandas.read_csv`
```
import pandas as pd

pd.read_csv("./my_path/train_data.csv")
```
And finally, after you have trained your model and done the predictions on the test set you can populate the lv EF column in the sample_submission.csv-file with your own predictions and submit it to this competition.

![echo animation](https://raw.githubusercontent.com/Bsingstad/DL-images/main/EchoAnimation.gif)
 
# Acknowledgements

This data set is derived from the [CAMUS data set](https://www.creatis.insa-lyon.fr/Challenge/camus/) and any scientific work using this data set should therefore cite the following source [5] 

# Reference
[1] https://www.heart.org/en/health-topics/heart-failure/diagnosing-heart-failure/ejection-fraction-heart-failure-measurement
[2] https://www.mayoclinic.org/tests-procedures/ekg/expert-answers/ejection-fraction/faq-20058286
[3] Simpson R, Bromage D, Dancy L, et al6 Comparing echocardiography and cardiac magnetic resonance measures of ejection fraction: implications for HFMRF researchHeart 2018;104:A3.
[4] https://www.acc.org/latest-in-cardiology/articles/2019/01/07/12/42/for-the-fits-the-future-of-cardiovascular-imaging-considerations-for-fits
[5] S. Leclerc, E. Smistad, J. Pedrosa, A. Ostvik, et al.
"Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography" in IEEE Transactions on Medical Imaging,
early acces, 2019