Toolbox for vessel X-ray angiography images simulation
In recent years, automatic computer techniques have been proven to be a great tool for the rapid detection and disease diagnosis. The core of those diagnostic systems are usually artificial intelligent algorithms like convolutional neural networks, in which thousands of images are needed for training. However, the available datasets of biomedical images, specially for X-ray angiography, are scarce. Therefore, we propose a toolbox for X-ray angiography images simulation to increase the number of available images as an alternative to data augmentation method for training artificial intelligence algorithms. The toolbox was developed with a set of functions to simulate complex vessel structures, as well as stenosis and aneurysms, in X-ray angiography images.
Antczak, K. and Liberadzki, Ł. (2018). Stenosis detection with deep convolutional neural networks. In MATEC Web of Conferences, volume 210, page 04001. EDP Sciences.
Babaee, M. and Nilchi, A. R. N. (2014). Synthetic data generation for x-ray imaging. In 2014 21th Iranian Conference on Biomedical Engineering (ICBME), pages 190–194. IEEE.
Castellino, R. A. (2005). Computer aided detection (cad): an overview. Cancer Imaging, 5(1):17.
Clark, A. (2015). Pillow (pil fork) documentation. readthedocs.
Dar, R. A., Rasool, M., Assad, A., et al. (2022). Breast cancer detection using deep learning: datasets, methods, and challenges ahead. Computers in Biology and Medicine, page 106073.
Freitas, S. A., Nienow, D., da Costa, C. A., and Ramos, G. d. O. (2021). Functional coronary artery assessment: a systematic literature review. Wiener klinische Wochenschrift, pages 1–17.
Freitas, S. A., Zeiser, F. A., Da Costa, C. A., and Ramos, G. D. O. (2022). Deepcadd: a deep learning architecture for automatic detection of coronary artery disease. In 2022 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE.
Harris, C. R., Millman, K. J., Van Der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., et al. (2020). Array programming with numpy. Nature, 585(7825):357–362.
Hunter, J. D. (2007). Matplotlib: A 2d graphics environment. Computing in science & engineering, 9(03):90–95.
Jia, D. and Zhuang, X. (2021). Learning-based algorithms for vessel tracking: A review. Computerized Medical Imaging and Graphics, page 101840.
Kengyelics, S. M., Treadgold, L. A., and Davies, A. G. (2018). X-ray system simulation software tools for radiology and radiography education. Computers in biology and medicine, 93:175–183.
Kim, D., Chung, J., Choi, J., Succi, M. D., Conklin, J., Longo, M. G. F., Ackman, J. B., Little, B. P., Petranovic, M., Kalra, M. K., et al. (2022). Accurate auto-labeling of chest x-ray images based on quantitative similarity to an explainable ai model. Nature communications, 13(1):1–15.
Kohlakala, A., Coetzer, J., Bertels, J., and Vandermeulen, D. (2022). Deep learning-based dental implant recognition using synthetic x-ray images. Medical & Biological Engineering & Computing, 60(10):2951–2968.
Lin, Y., Zhang, H., and Hu, G. (2018). Automatic retinal vessel segmentation via deeply supervised and smoothly regularized network. IEEE Access, 7:57717–57724.
Morís, D. I., de Moura, J., Novo, J., and Ortega, M. (2021). Portable chest x-ray synthetic image generation for the covid-19 screening. Engineering Proceedings, 7(1):6.
Ovalle-Magallanes, E., Avina-Cervantes, J. G., Cruz-Aceves, I., and Ruiz-Pinales, J. (2022). Improving convolutional neural network learning based on a hierarchical bezier generative model for stenosis detection in x-ray images. Computer Methods and Programs in Biomedicine, 219:106767.
Ponti, M. A., dos Santos, F. P., Ribeiro, L. S., and Cavallari, G. B. (2021). Training deep networks from zero to hero: avoiding pitfalls and going beyond. In 2021 34th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pages 9–16. IEEE.
Ramasamy, A., Chen, Y., Zanchin, T., Jones, D. A., Rathod, K., Jin, C., Onuma, Y., Zhang, Y.-J., Amersey, R., Westwood, M., et al. (2020). Optical coherence tomography enables more accurate detection of functionally significant intermediate non-left main coronary artery stenoses than intravascular ultrasound: a meta-analysis of 6919 patients and 7537 lesions. International Journal of Cardiology, 301:226–234.
Shah, P. M., Ullah, H., Ullah, R., Shah, D., Wang, Y., Islam, S. u., Gani, A., and Rodrigues, J. J. (2022). Dc-gan-based synthetic x-ray images augmentation for increasing the performance of efficientnet for covid-19 detection. Expert Systems, 39(3):e12823.
Shen, D., Wu, G., and Suk, H.-I. (2017). Deep learning in medical image analysis. Annual review of biomedical engineering, 19:221.
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., and Summers, R. M. (2016). Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging, 35(5):1285–1298.
Song, S., Yang, J., Fan, J., Cong, W., Ai, D., Zhao, Y., and Wang, Y. (2016). Geometrical force constraint method for vessel and x-ray angiogram simulation. Journal of X-ray Science and Technology, 24(1):87–106.
Srivastav, D., Bajpai, A., and Srivastava, P. (2021). Improved classification for pneumonia detection using transfer learning with gan based synthetic image augmentation. In 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pages 433–437. IEEE.
Togo, R., Ogawa, T., and Haseyama, M. (2019). Synthetic gastritis image generation via loss function-based conditional pggan. IEEE access, 7:87448–87457.
Vijayalakshmi, I. (2015). Cardiac Catheterization and Imaging (From Pediatrics to Geriatrics). JP Medical Ltd.
World Health Organization (2021). Cardiovascular disease (cvds). [link].