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| EchoNet-Dynamic:<br/>Interpretable AI for beat-to-beat cardiac function assessment | |
| ------------------------------------------------------------------------------ | |
| EchoNet-Dynamic is a end-to-end beat-to-beat deep learning model for | |
| 1) semantic segmentation of the left ventricle | |
| 2) prediction of ejection fraction by entire video or subsampled clips, and | |
| 3) assessment of cardiomyopathy with reduced ejection fraction. | |
| For more details, see the accompanying paper, | |
| > [**Video-based AI for beat-to-beat assessment of cardiac function**](https://www.nature.com/articles/s41586-020-2145-8)<br/> | |
| David Ouyang, Bryan He, Amirata Ghorbani, Neal Yuan, Joseph Ebinger, Curt P. Langlotz, Paul A. Heidenreich, Robert A. Harrington, David H. Liang, Euan A. Ashley, and James Y. Zou. <b>Nature</b>, March 25, 2020. https://doi.org/10.1038/s41586-020-2145-8 | |
| Dataset | |
| ------- | |
| We share a deidentified set of 10,030 echocardiogram images which were used for training EchoNet-Dynamic. | |
| Preprocessing of these images, including deidentification and conversion from DICOM format to AVI format videos, were performed with OpenCV and pydicom. Additional information is at https://echonet.github.io/dynamic/. These deidentified images are shared with a non-commerical data use agreement. | |
| Examples | |
| -------- | |
| We show examples of our semantic segmentation for nine distinct patients below. | |
| Three patients have normal cardiac function, three have low ejection fractions, and three have arrhythmia. | |
| No human tracings for these patients were used by EchoNet-Dynamic. | |
| | Normal | Low Ejection Fraction | Arrhythmia | | |
| | ------ | --------------------- | ---------- | | |
| |  |  |  | | |
| |  |  |  | | |
| |  |  |  | | |
| Installation | |
| ------------ | |
| First, clone this repository and enter the directory by running: | |
| git clone https://github.com/echonet/dynamic.git | |
| cd dynamic | |
| EchoNet-Dynamic is implemented for Python 3, and depends on the following packages: | |
| - NumPy | |
| - PyTorch | |
| - Torchvision | |
| - OpenCV | |
| - skimage | |
| - sklearn | |
| - tqdm | |
| Echonet-Dynamic and its dependencies can be installed by navigating to the cloned directory and running | |
| pip install --user . | |
| Usage | |
| ----- | |
| ### Preprocessing DICOM Videos | |
| The input of EchoNet-Dynamic is an apical-4-chamber view echocardiogram video of any length. The easiest way to run our code is to use videos from our dataset, but we also provide a Jupyter Notebook, `ConvertDICOMToAVI.ipynb`, to convert DICOM files to AVI files used for input to EchoNet-Dynamic. The Notebook deidentifies the video by cropping out information outside of the ultrasound sector, resizes the input video, and saves the video in AVI format. | |
| ### Setting Path to Data | |
| By default, EchoNet-Dynamic assumes that a copy of the data is saved in a folder named `a4c-video-dir/` in this directory. | |
| This path can be changed by creating a configuration file named `echonet.cfg` (an example configuration file is `example.cfg`). | |
| ### Running Code | |
| EchoNet-Dynamic has three main components: segmenting the left ventricle, predicting ejection fraction from subsampled clips, and assessing cardiomyopathy with beat-by-beat predictions. | |
| Each of these components can be run with reasonable choices of hyperparameters with the scripts below. | |
| We describe our full hyperparameter sweep in the next section. | |
| #### Frame-by-frame Semantic Segmentation of the Left Ventricle | |
| echonet segmentation --save_video | |
| This creates a directory named `output/segmentation/deeplabv3_resnet50_random/`, which will contain | |
| - log.csv: training and validation losses | |
| - best.pt: checkpoint of weights for the model with the lowest validation loss | |
| - size.csv: estimated size of left ventricle for each frame and indicator for beginning of beat | |
| - videos: directory containing videos with segmentation overlay | |
| #### Prediction of Ejection Fraction from Subsampled Clips | |
| echonet video | |
| This creates a directory named `output/video/r2plus1d_18_32_2_pretrained/`, which will contain | |
| - log.csv: training and validation losses | |
| - best.pt: checkpoint of weights for the model with the lowest validation loss | |
| - test_predictions.csv: ejection fraction prediction for subsampled clips | |
| #### Beat-by-beat Prediction of Ejection Fraction from Full Video and Assesment of Cardiomyopathy | |
| The final beat-by-beat prediction and analysis is performed with `scripts/beat_analysis.R`. | |
| This script combines the results from segmentation output in `size.csv` and the clip-level ejection fraction prediction in `test_predictions.csv`. The beginning of each systolic phase is detected by using the peak detection algorithm from scipy (`scipy.signal.find_peaks`) and a video clip centered around the beat is used for beat-by-beat prediction. | |
| ### Hyperparameter Sweeps | |
| The full set of hyperparameter sweeps from the paper can be run via `run_experiments.sh`. | |
| In particular, we choose between pretrained and random initialization for the weights, the model (selected from `r2plus1d_18`, `r3d_18`, and `mc3_18`), the length of the video (1, 4, 8, 16, 32, 64, and 96 frames), and the sampling period (1, 2, 4, 6, and 8 frames). | |