With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

We propose a novel deep learning based system for vessel segmentation. Existing methods
using CNNs have mostly relied on local appearances learned on the regular image grid …

Deep learning (DL) has become widely used for medical image segmentation in recent
years. However, despite these advances, there are still problems for which DL-based …

Open international challenges are becoming the de facto standard for assessing computer
vision and image analysis algorithms. In recent years, new methods have extended the …

Detection of coronary artery stenosis in coronary CT angiography (CCTA) requires highly
personalized surface meshes enclosing the coronary lumen. In this work, we propose to use …

We present an end-to-end deep learning segmentation method by combining a 3D UNet
architecture with a graph neural network (GNN) model. In this approach, the convolutional …

Precise segmentation of the lung airways is essential for a quantitative assessment of airway
diseases. However, because of the complexity of the airway structure and the different …

Segmenting objects with indistinct boundaries and large variations from CT volumes is a
challenging issue due to overlap** intensity distributions from neighboring tissues or long …

Airway semantic segmentation, which refers to segmenting airway from background and
dividing it into anatomical segments, provides clinically valuable information for lung lobe …