Deep learning (DL) algorithms have rapidly become a robust tool for analyzing medical
images. They have been used extensively for medical image segmentation as the first and …

Background Automated segmentation of anatomical structures is a crucial step in image
analysis. For lung segmentation in computed tomography, a variety of approaches exists …

Variations in the shape and appearance of anatomical structures in medical images are
often relevant radiological signs of disease. Automatic tools can help automate parts of this …

Recently, agricultural remote sensing community has endeavored to utilize the power of
artificial intelligence (AI). One important topic is using AI to make the mapping of crops more …

The use of artificial intelligence (AI) in the segmentation of liver structures in medical images
has become a popular research focus in the past half-decade. The performance of AI tools in …

When dealing with a newly emerging disease such as COVID-19, the impact of patient-and
disease-specific factors (eg, body weight or known co-morbidities) on the immediate course …

Whole body oncological screening using CT images requires a good anatomical localisation
of organs and the skeleton. While a number of algorithms for multi-organ localisation have …

Currently, increasingly large medical imaging data sets become available for research and
are analysed by a range of algorithms segmenting anatomical structures automatically and …

A key question in learning from clinical routine imaging data is whether we can identify
coherent patterns that re-occur across a population, and at the same time are linked to …

Non-contrast CT is often preferred in clinical screening while segmentation of such CT data
is more challenging due to the low contrast in tissue boundaries and scarce supervised …