Weakly-supervised 3D coronary artery reconstruction from two-view angiographic images
This work addresses a critical need in clinical practice for accurate 3D artery reconstruction to assist in diagnosis and surgery, representing a strong specific gain in medical imaging.
The paper tackles the problem of reconstructing 3D coronary artery models from two-view angiographic images, achieving reconstruction accuracies that outperform state-of-the-art techniques through a method combining 3D fully supervised and 2D weakly supervised learning.
The reconstruction of three-dimensional models of coronary arteries is of great significance for the localization, evaluation and diagnosis of stenosis and plaque in the arteries, as well as for the assisted navigation of interventional surgery. In the clinical practice, physicians use a few angles of coronary angiography to capture arterial images, so it is of great practical value to perform 3D reconstruction directly from coronary angiography images. However, this is a very difficult computer vision task due to the complex shape of coronary blood vessels, as well as the lack of data set and key point labeling. With the rise of deep learning, more and more work is being done to reconstruct 3D models of human organs from medical images using deep neural networks. We propose an adversarial and generative way to reconstruct three dimensional coronary artery models, from two different views of angiographic images of coronary arteries. With 3D fully supervised learning and 2D weakly supervised learning schemes, we obtained reconstruction accuracies that outperform state-of-art techniques.