Deep learning has received extensive research interest in developing new medical image
processing algorithms, and deep learning based models have been remarkably successful …

The loss function is an important component in deep learning-based segmentation methods.
Over the past five years, many loss functions have been proposed for various segmentation …

Despite the considerable progress in automatic abdominal multi-organ segmentation from
CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is …

Abstract Fully Convolutional Neural Networks (FCNNs) with contracting and expanding
paths have shown prominence for the majority of medical image segmentation applications …

With the unprecedented developments in deep learning, automatic segmentation of main
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …

Deep learning-based image segmentation is by now firmly established as a robust tool in
image segmentation. It has been widely used to separate homogeneous areas as the first …

Image registration is a fundamental medical image analysis task, and a wide variety of
approaches have been proposed. However, only a few studies have comprehensively …

Automated detection of curvilinear structures, eg, blood vessels or nerve fibres, from medical
and biomedical images is a crucial early step in automatic image interpretation associated to …

Recent advances in transformer-based models have drawn attention to exploring these
techniques in medical image segmentation, especially in conjunction with the U-Net model …

Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …