Plaque Detection in Carotid Artery using Image Processing
Keywords:
U-Net, Plaque in carotid artery- Superimposition, Ultrasonic Image, SegmentationAbstract
Carotid artery plaque is a major contributor to ischemic strokes, making its early identification essential for effective treatment and prevention. The intricate structure of the carotid artery, combined with the subtle characteristics of early stage plaque, complicates timely diagnosis, often leading to treatment delays that negatively impact patient outcomes. This paper explores image processing methods and AI-driven
approaches for the early identification and precise segmentation of carotid artery plaque. It specifically focuses on computer-aided detection (CAD) systems that utilize convolutional neural networks (CNNs) and U-Net-based frameworks within the realm of medical imaging, particularly with complex modalities like ultrasound, CT, and MRI scans.
These techniques emphasize important diagnostic metrics such as the Dice Similarity Coefficient, sensitivity, and specificity, which are vital for assessing the accuracy of plaque identification and the overall reliability of diagnoses
Models based on CNNs can detect plaque by differentiating it from adjacent arterial tissue using enhanced and pre-processed images, thus minimizing the limitations and biases associated with traditional imaging interpretations by radiologists. The U-Net architecture, tailored for medical image segmentation, effectively captures subtle variations in tissue texture within the carotid artery, making it beneficial for early detection. This
model processes imaging data in a manner that reduces the chances of misdiagnosis and delays, alleviating the pressure on healthcare providers while significantly improving diagnostic accuracy.
Downloads
Metrics
References
Hajhosseiny R., Bahaei T. S., Prieto C., Botnar R. M. (2019). Molecular and nonmolecular magnetic resonance coronary and carotid imaging. Arterioscler. Thromb. Vasc. Biol. 39, 569–582. doi: 10.1161/ATVBAHA.118.311754, PMID
Hu S., Cui B., Mlynash M., Zhang X., Mehta K. M., Lansberg M. G. (2020). Stroke epidemiology and stroke policies in China from 1980 to 2017: a systematic review and metaanalysis. Int. J. Stroke 15, 18–28. doi: 10.1177/1747493019873562, PMID
Chen, P. T., Wu, T., Wang, P., Chang, D.,
Liu, K. L., Wu, M. S., ... & Wang, W. (2023).
Pancreatic cancer detection on CT scans with deep learning: a nationwide population-based study. Radiology, 306(1), 172-182.
Jain P. K., Sharma N., Kalra M. K., Johri A., Saba L., Suri J. S. (2022). Far wall plaque segmentation and area measurement in common and internal carotid artery ultrasound using Useries architectures: an unseen artificial intelligence paradigm for stroke risk assessment. Comput.Biol.Med.149:106017.doi:10.1016/j.com pbiomed.2022.106017
Johri A. M., Nambi V., Naqvi T. Z., Feinstein S. B., Kim E. S. H., Park M. M., et al. (2020). Recommendations for the assessment of carotid arterial plaque by ultrasound for the characterization of atherosclerosis and evaluation of cardiovascular risk: from the American Society of Echocardiography. J. Am. Soc. Echocardiogr. 33, 917–933. doi: 10.1016/j.echo.2020.04.021.
Murray C. S. G., Nahar T., Kalashyan H., Becher H., Nanda N. C. (2018). Ultrasound assessment of carotid arteries: current concepts, methodologies, diagnostic criteria, and technological advancements. Echocardiography 35, 2079–2091. doi: 10.1111/echo.14197
van Dam-Nolen D. H. K., Truijman M. T. B., van der Kolk A. G., Liem M. I., Schreuder F., Boersma E., et al.(2022). Carotid plaque characteristics predict recurrent ischemic stroke and TIA: the PARISK (plaque at RISK) study. JACC Cardiovasc. Imaging 15, 1715–1726. doi:
10.1016/j.jcmg.2022.04.003
Wang L., Shao A., Huang F., Liu Z., Wang Y., Huang X., et al. . (2023). Deep learning-based semantic segmentation of non-melanocytic skin tumors in whole-slide histopathological images. Exp. Dermatol. 32, 831–839. doi:
10.1111/exd.14782
Ying C., Li X., Lv S., Du P., Chen Y., Fu H.,
et al. . (2022). T-SPOT with CT image analysis based on deep learning for early differential diagnosis of nontuberculous mycobacteria pulmonary disease and pulmonary tuberculosis.
Int. J. Infect. Dis. 125, 42–50. doi: 10.1016/j.ijid.2022.09.031
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
