Radiomics is an emerging translational field of research aiming to extract mineable high-
dimensional data from clinical images. The radiomic process can be divided into distinct …

“Radiomics” refers to the extraction and analysis of large amounts of advanced quantitative
imaging features with high throughput from medical images obtained with computed …

Accurate lung nodule segmentation from computed tomography (CT) images is of great
importance for image-driven lung cancer analysis. However, the heterogeneity of lung …

Artificial intelligence (AI) has been a very active research topic over the last years and
thoracic imaging has particularly benefited from the development of AI and in particular deep …

An accurate segmentation of lung nodules in computed tomography (CT) images is critical to
lung cancer analysis and diagnosis. However, due to the variety of lung nodules and the …

Accurate volumetric assessment in non-small cell lung cancer (NSCLC) is critical for
adequately informing treatments. In this study we assessed the clinical relevance of a …

Medical image segmentation is a fundamental and challenging problem for analyzing
medical images. Among different existing medical image segmentation methods, graph …

Radiomics uses advanced image analysis to extract massive amounts of quantitative
information from digital images, which is not otherwise distinguishable to the human eye …

A single click ensemble segmentation (SCES) approach based on an existing “Click &
Grow” algorithm is presented. The SCES approach requires only one operator selected …

It is difficult to obtain an accurate segmentation due to the variety of lung nodules in
computed tomography (CT) images. In this study, we propose a data-driven model, called …