Designing deep learning based methods with medical images has always been an attractive
area of research to assist clinicians in rapid examination and accurate diagnosis. Those …

In the realm of medical imaging, the training of machine learning models necessitates a
large and varied training dataset to ensure robustness and interoperability. However …

The morphology of retinal vessels is closely associated with many kinds of ophthalmic
diseases. Although huge progress in retinal vessel segmentation has been achieved with …

Accurate and up-to-date maps of built-up areas are crucial to support sustainable urban
development. Earth Observation (EO) is a valuable data source to cover this demand. In …

Recent advancements in deep learning architectures have extended their application to
computer vision tasks, one of which is the segmentation of retinal blood vessels from retinal …

The structure of the retinal vascular tree can reflect many indicators of ophthalmic health
status. Retinal vessel segmentation, an important basis for the quantitative analysis of …

Most existing deep learning based methods for vessel segmentation neglect two important
aspects of retinal vessels: The orientation information of vessels and the contextual …

Vessel segmentation is crucial in many medical image applications, such as detecting
coronary stenoses, retinal vessel diseases and brain aneurysms. However, achieving high …

Retinal vessel segmentation based on deep learning is an important auxiliary method for
assisting clinical doctors in diagnosing retinal diseases. However, existing methods often …

The generalization ability of the fetal head segmentation method is reduced due to the data
obtained by different machines, settings, and operations. To keep the generalization ability …