Publications by authors named "Gyu-Jun Jeong"
Coronary artery calcium (CAC) scoring CT is a useful tool for screening coronary artery disease and for cardiovascular risk stratification. However, its efficacy in patients with coronary stents, who had pre-existing coronary artery disease, remains uncertain. Historically, CAC CT scans of these patients have been manually excluded from the CAC scoring process, even though most of the CAC scoring process is now fully automated.
View Article and Find Full Text PDFDeep learning model for intravascular ultrasound image segmentation with temporal consistency.
Int J Cardiovasc Imaging
November 2024
This study was conducted to develop and validate a deep learning model for delineating intravascular ultrasound (IVUS) images of coronary arteries.Using a total of 1240 40-MHz IVUS pullbacks with 191,407 frames, the model for lumen and external elastic membrane (EEM) segmentation was developed. Both frame- and vessel-level performances and clinical impact of the model on 3-year cardiovascular events were evaluated in the independent data sets.
View Article and Find Full Text PDFArticle Synopsis
- Aortic dissection (AD) is a critical condition that needs quick and accurate diagnosis, and this study introduces a new method called ZOZI-seg for better segmentation of aorta in CT images.* -
- The ZOZI-seg method combines a 3D transformer and a 3D UNet, utilizing a two-stage architecture to capture both the overall anatomy and fine details of the aorta’s compartments: the true lumen, false lumen, and thrombosis.* -
- Results show that ZOZI-seg performs better than existing models with high segmentation accuracy, suggesting it could enhance AD diagnosis and treatment in clinical settings and warrants further research.*
Background And Objectives: Intravascular ultrasound (IVUS) evaluation of coronary artery morphology is based on the lumen and vessel segmentation. This study aimed to develop an automatic segmentation algorithm and validate the performances for measuring quantitative IVUS parameters.
Methods: A total of 1,063 patients were randomly assigned, with a ratio of 4:1 to the training and test sets.