Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp24-cday-program.php
Event Website
"https://github.com/CS-4850-Spring-2024-Project-CZ-1-CNN "
Document Type
Event
Start Date
25-4-2024 4:00 PM
Description
The project contributes to the advancement of medical imaging technology by overcoming the challenges associated with segmenting coronary arteries from ICA images. By leveraging deep learning algorithms, the system can effectively extract coronary arteries with high accuracy, providing valuable information for CAD diagnosis and treatment planning. Accurate and efficient coronary artery segmentation can improve the workflow of cardiologists and enhance the quality of patient care. A robust automated segmentation model could potentially reduce the time and resources required for manual annotation by experienced cardiologists, leading to cost savings and increased efficiency in clinical settings. Additionally, the developed model could be integrated into the education system with an interactive GUI for cardiologists to draw and learn the anatomical structure of the coronary artery system.
Included in
UR-49 Coronary Artery Segmentation Using Convolutional Neural Network
https://www.kennesaw.edu/ccse/events/computing-showcase/sp24-cday-program.php
The project contributes to the advancement of medical imaging technology by overcoming the challenges associated with segmenting coronary arteries from ICA images. By leveraging deep learning algorithms, the system can effectively extract coronary arteries with high accuracy, providing valuable information for CAD diagnosis and treatment planning. Accurate and efficient coronary artery segmentation can improve the workflow of cardiologists and enhance the quality of patient care. A robust automated segmentation model could potentially reduce the time and resources required for manual annotation by experienced cardiologists, leading to cost savings and increased efficiency in clinical settings. Additionally, the developed model could be integrated into the education system with an interactive GUI for cardiologists to draw and learn the anatomical structure of the coronary artery system.
https://digitalcommons.kennesaw.edu/cday/Spring_2024/Undergraduate_Research/4