AngioData : A Novel Open Dataset of Coronary Artery Keyframes Extracted via Deep Learning

This is the official repository for the paper "AngioData : A Novel Open Dataset of Coronary Artery Keyframes Extracted via Deep Learning" submited in Journal of Cardiovascular Translational Research.

We're applying AngioKey for image segmentation, followed by extracting keyframes from the segmented images.

AngiKey Framework

I have to say that this code was inspired from https://github.com/RGivisiez/Blood-Vessel-Segmentation.

I made some of the following changes:

• Load images has been adapted to the structure of our dataset;
• We trained the U-net model on a dataset given from https://doi.org/10.3390/app9245507;
• The test is done on our personal dataset to produce the frame masks (Ground Truth).

Keyframes extraction

The keyframe extraction work has been featured in publication https://www.scitepress.org/PublicationsDetail.aspx?ID=QhrTWXiEsD8=&t=1.

usefulness :

This project can be used to automatically generate groundtruths of medical images. It was tested on a private dataset of angiograms. We present her a sample of coronary frames extracted from angiographic videos. We segmented all frames and extracted all keyframes from each sampled video.

The project will be useful to automatically manage a large dataset ensuring better performance and extract keyframes.

Authors:

Hounaida Moalla & Aiman Ghrab

Previous works

Previous work has been presented in

Moalla, H.; Ghrab, A.; Ben Hamed, B.; Bahloul, A.; Hammami, R. and Abid, L. (2023). Automatic Coronary Angiogram Keyframe Extraction. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods, ISBN 978-989-758-626-2, ISSN 2184-4313, pages 582-589. doi: https://doi.org/10.1109/INISTA59065.2023.10310496

In this work, we have applied the method of filters on the frames for extraction of keyframes.

Other uses

1- We used the segmented keyframes for classification Left/Right of frames. H. Moalla, A. Ghrab, B. B. Hamed, A. Bahloul and L. Abid, "Exploiting Pre-trained Architectures for Dual-Stream Classification of LCA-RCA in a Private AngioData," 2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA), Hammamet, Tunisia, 2023, pp. 1-6. doi: 10.1109/INISTA59065.2023.10310496.

2- Next step will be focuse on quantification of stenosis : detection and mesure.

Citation

@software{Hounaidaandall_2023, authors = {Hounaida Moalla and Aiman Ghrab}, doi = {10.5281/zenodo.1234}, month = {04}, title = {{Coronary-Angiogram-Keyframes-extraction}}, url = {https://github.com/HounaidaM/Coronary-Angiogram-Keyframes-extraction}, version = {v1.4.23}, year = {2023} }

Environment

Python, Keras, Kaggle cloud

AngioData Dataset

The full dataset was collected from exams performed by a single catheterization laboratory during the period between January 2018 and December 2021. Dataset consisted of 3159 angiographic study: a total of 37209 coronary angiograms was extracted. We used a sample of 45 angiograms to extract a total of 1434 frames of size 512 x 512 pixels. A sample of the dataset is put in the "oneSampleCoro" section above. All zipped dataset is put in the "alldataSetdeep" section above.

Model

The used model is U-net from https://arxiv.org/abs/1505.04597.

Trained models

Section Models contains models traindes with 150, 200 and 300 epochs.