<img src='imgs/landmarks.gif' align="right" width=440>

[[paper](https://www.frontiersin.org/articles/10.3389/fcvm.2021.730316/full)] [[installation](https://github.com/moralesq/DeepStrain/tree/main/installation)] [[examples](https://github.com/moralesq/DeepStrain/tree/main/examples)]

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# DeepStrain

**DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics.**  

:sparkles: **New Updates.**

- :rocket: Trained weights moved to Harvard Dataverse: https://doi.org/10.7910/DVN/XB6PEZ. 

- :rocket: Weights script updated for easy download: bash ./pretrained_models/download_model.sh

- :rocket: Cardiac Motion Analysis Challenge (CMAC) dataset uploaded to Dataverse!!! 

- :rocket: CMAC script available for easy download: bash ./datasets/download_cmac.sh

- :rocket: Code to read CMAC landmarks uploaded (./cmac) see [demo](https://github.com/moralesq/DeepStrain/blob/main/examples/1_inspect_acdc_dataset.ipynb).

Tensorflow implementation for cardiac segmentation, motion estimation, and strain analysis from balanced steady-state free-precession (bSSFP) cine MRI images.

**Note**: The current software works well with Tensorflow 2.3.1+.

**UPDATE 2024**: The trained moel is now available through the Harvard Dataverse: https://doi.org/10.7910/DVN/XB6PEZ

## Evaluation

<p align="center">

    DeepStrain Tracking in Patients Across MRI Vendors

    <br>

    <img src='imgs/DeepStrain_vs_CVI_Vid3.gif' width=440>

    <br>

    <p align="justify">

    Short-axis bSSFP cine MRI images are shown at the mid-ventricle. To visualize tracking, myocardial contours of the endocardial (red) and epicardial (green) left ventricular wall defined at end diastole were deformed to end systole using displacement vectors based on DeepStrain. Vendor 1 = 1.5T (Achieva; Philips Medical Systems, Best, the Netherlands). Vendor 2 = 3T (MAGNETOM Vida; Siemens Healthcare, Erlangen, Germany). 

    </p>

    <br><br>

    Evaluation Relative to Feature Tracking in Patients

    <br>

    <img src='imgs/DeepStrain_vs_CVI_figure_4.png' width=840>

    <br>

    <p align="justify">

    Measurements of global radial and circumferential strain derived from cvi42 feature tracking were compared to those derived with DeepStrain. Each dot represents a single subject (n = 580). Solid line shows linear regression analyses based on images acquired in Philips (blue) and Siemens (red) MRI scanners. On the left, the slopes (y = 1.06×, y = 1.04×) represent a -6% and -4% disagreement in measurements of global radial strain. The slopes (y = 0.99×, y = 1.00×) on the right represent a 1% and 0% disagreement in measurements of global circumferential strain. 

    </p>

    <br>

</p>

#### Agreement Between cvi42 Feature Tracking and DeepStrain

| Linear Regression Slope | GRS               | GCS               | 

| ----------------------- | ----------------- | ----------------- | 

| Vendor 1 (n = 466)      | 1.06 (1.03, 1.08) | 0.98 (0.98, 1.01) |

| Vendor 2 (n = 114)      | 1.04 (0.99, 1.09) | 1.0 (0.96, 1.03)  |

| Total (n = 580)         | 1.05 (1.03, 1.08) | 0.99 (0.98, 1.01) |

| Pearson Correlation Coefficient (r) | GRS  | GCS  | 

| ----------------------------------- | ---- | -----| 

| Vendor 1 (n = 466)                  | 0.85 | 0.91 |

| Vendor 2 (n = 114)                  | 0.83 | 0.88 |

| Total (n = 580)                     | 0.85 | 0.91 |

GRC = global radial strain; GCS = global circumferential strain. 

Vendor 1 = 1.5T (Achieva; Philips Medical Systems, Best, the Netherlands); Vendor 2 = 3T (MAGNETOM Vida; Siemens Healthcare, Erlangen, Germany).

Data are the slope (95% confidence interval of the slope) and Pearson correlation coefficient (r).

## Application

<p align="center">

    <img src='imgs/figure_4_Global_SRe.jpg' width=840>

    <br>

    <p align="justify">

    The study cohort consisted of 119 participants (35 ± 5 years, 50% male) including the control group with 30 subjects; RFG1 with 39 overweight subjects; RFG2 with 30 hypertensive subjects, including 13 (43%) with additional overweight; RFG3 with 20 T2DM subjects, including 11 (55%) with additional overweight, 1 (5%) with additional hypertension and 8 (40%) with both. Measures of (a) circumferential and (b) radial early-diastolic strain rate (SR) in controls and risk factor groups. Strain results are visualized as mean with 95% confidence interval. Post-hoc test by Bonferroni: * p < 0.05; ** p < 0.01; *** p < 0.001. RFG = risk factor group.

    </p>

    <br><br>

</p>

## Publications

If you find DeepStrain or some part of the code useful, please cite as appropiate:

- **DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics.** [Manuel A. Morales](https://catanalab.martinos.org/lab-members/manuel-a-morales/), [Maaike van den Boomen](https://nguyenlab.mgh.harvard.edu/maaike-van-den-boomen-ms/), [Christopher Nguyen](https://nguyenlab.mgh.harvard.edu/christopher-nguyen-phd-2/), [Jayashree Kalpathy-Cramer](https://www.ccds.io/leadership-team/jayashree-kalpathy-cramer/), [Bruce R. Rosen](https://www.martinos.org/investigator/bruce-rosen/), [Collin M. Stultz](https://mitibmwatsonailab.mit.edu/people/collin-m-stultz/), [David Izquierdo-Garcia](https://catanalab.martinos.org/lab-members/david-izquierdo-garcia/),  [Ciprian Catana](https://catanalab.martinos.org/lab-members/ciprian-catana/). Frontiers in Cardiovascular Medicine, 2021. DOI: https://doi.org/10.3389/fcvm.2021.730316.

- **DeepStrain Evidence of Asymptomatic Left Ventricular Diastolic and Systolic Dysfunction in Young Adults With Cardiac Risk Factors.** [Manuel A. Morales](https://catanalab.martinos.org/lab-members/manuel-a-morales/), Gert J. H. Snel, [Maaike van den Boomen](https://nguyenlab.mgh.harvard.edu/maaike-van-den-boomen-ms/), Ronald J. H. Borra, Vincent M. van Deursen, Riemer H. J. A. Slart, [David Izquierdo-Garcia](https://catanalab.martinos.org/lab-members/david-izquierdo-garcia/), Niek H. J. Prakken,  [Ciprian Catana](https://catanalab.martinos.org/lab-members/ciprian-catana/). Frontiers in Cardiovascular Medicine, 2022. DOI: https://doi.org/10.3389/fcvm.2022.831080

- **Comparison of DeepStrain and Feature Tracking for Cardiac MRI Strain Analysis.** [Manuel A. Morales](https://cardiacmr.hms.harvard.edu/people/manuel-morales-phd), [Julia Cirillo](https://cardiacmr.hms.harvard.edu/people/julia-cirillo), [Kei Nakata](https://cardiacmr.hms.harvard.edu/people/kei-nakata-md-phd), [Selcuk Kucukseymen](https://cardiacmr.hms.harvard.edu/people/selcuk-kucukseymen-md), [Long H. Ngo](https://www.bidmc.org/research/research-by-department/medicine/general-medicine-research/research-faculty/long-h-ngo-phd), [David Izquierdo-Garcia](https://catanalab.martinos.org/lab-members/david-izquierdo-garcia/),  [Ciprian Catana](https://catanalab.martinos.org/lab-members/ciprian-catana/), [Reza Nezafat](https://cardiacmr.hms.harvard.edu/people/reza-nezafat). Journal of Magnetic Resonance Imaging, 2022. DOI: