Publications by authors named "Drew Pienta"
Computational hemodynamics is increasingly being used to quantify hemodynamic characteristics in and around abdominal aortic aneurysms (AAA) in a patient-specific fashion. However, the time-consuming manual annotation hinders the clinical translation of computational hemodynamic analysis. Thus, we investigate the feasibility of using deep-learning-based image segmentation methods to reduce the time required for manual segmentation.
View Article and Find Full Text PDFAutomatic extraction of coronary arteries using deep learning in invasive coronary angiograms.
Technol Health Care
December 2023
Background: Accurate extraction of coronary arteries from invasive coronary angiography (ICA) images is essential for the diagnosis and risk stratification of coronary artery disease (CAD).
Objective: In this study, a novel deep learning (DL) method is proposed for automatically extracting coronary arteries from ICA images.
Methods: A convolutional neural network (CNN) was developed with full-scale skip connections and full-scale deep supervisions.
Article Synopsis
- Automated semantic labeling of coronary arterial segments in invasive coronary angiography (ICA) is key for improving the diagnosis of coronary artery disease (CAD), but is complicated by the morphological similarities of arterial branches and human variability.
- The study proposes an association graph-based graph matching network (AGMN) to accurately label these segments by converting the task into a vertex classification problem, using graphs to represent relationships between arterial segments.
- The model, validated with a dataset of 263 ICAs, achieved high performance metrics (average accuracy of 0.8264, precision of 0.8276, recall of 0.8264, and F1-score of 0.8262), significantly surpassing prior methods in semantic labeling of coronary
Background: Coronary artery disease (CAD) is the leading cause of death in the United States (US) and a major contributor to healthcare cost. Accurate segmentation of coronary arteries and detection of stenosis from invasive coronary angiography (ICA) are crucial in clinical decision making.
Purpose: We aim to develop an automatic method to extract coronary arteries by deep learning and detect arterial stenosis from ICAs.