Deep learning is a genre of machine learning that allows computational models to learn
representations of data with multiple levels of abstraction using numerous processing layers …

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 …

Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or
roads, is relevant to many fields of research. For such structures, the topology is their most …

Data availability plays a critical role for the performance of deep learning systems. This
challenge is especially acute within the medical image domain, particularly when …

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 …

Gross tumor volume (GTV) and clinical target volume (CTV) delineation are two critical steps
in the cancer radiotherapy planning. GTV defines the primary treatment area of the gross …

Extensive research has been devoted to the segmentation of the coronary artery. However,
owing to its complex anatomical structure, it is extremely challenging to automatically …

We propose a novel airway segmentation method in volumetric chest computed tomography
(CT) and evaluate its performance on multiple datasets. The segmentation is performed …

Training convolutional neural networks (CNNs) for segmentation of pulmonary airway,
artery, and vein is challenging due to sparse supervisory signals caused by the severe class …

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 …