Various structures in human physiology follow a treelike morphology, which often expresses
complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal …

Over the last decade, deep learning (DL) has contributed to a paradigm shift in computer
vision and image recognition creating widespread opportunities of using artificial …

Automated airway segmentation is a prerequisite for pre-operative diagnosis and intra-
operative navigation for pulmonary intervention. Due to the small size and scattered spatial …

Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung
diseases, while its manual delineation is unduly burdensome. To alleviate this time …

This paper presents a fully automatic and end-to-end optimised airway segmentation
method for thoracic computed tomography, based on the U-Net architecture. We use a …

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and
prognosis in pulmonary diseases. However, the manual delineation of airway structures …

Airway segmentation is critical for virtual bronchoscopy and computer-aided pulmonary
disease analysis. In recent years, convolutional neural networks (CNNs) have been widely …

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

Automated airway segmentation models often suffer from discontinuities in peripheral
bronchioles, which limits their clinical applicability. Furthermore, data heterogeneity across …

Automatic lung organ segmentation on computerized tomography images is crucial for lung
disease diagnosis. However, the unlimited voxel values and class imbalance of lung organs …