Deep learning-based semi-supervised learning (SSL) algorithms have led to promising
results in medical images segmentation and can alleviate doctors' expensive annotations by …

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 …

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark
(LiTS), which was organized in conjunction with the IEEE International Symposium on …

The active contour model (ACM) approach in image segmentation is regarded as a research
hotspot in the area of computer vision, which is widely applied in different kinds of …

Image classification is the act of labeling groups of pixels or voxels of an image based on
some rules. It finds applications in medical image analysis, and satellite image identification …

Contrastive learning (CL) aims to learn useful representation without relying on expert
annotations in the context of medical image segmentation. Existing approaches mainly …

Deep neural networks have achieved great success in medical image segmentation
problems such as liver, kidney, the accuracy of which already exceeds the human level …

In clinical practice, it is desirable for medical image segmentation models to be able to
continually learn on a sequential data stream from multiple sites, rather than a consolidated …

High‐precision image segmentation of the spine in computed tomography (CT) images is
important for the diagnosis of spinal diseases and surgical path planning. Manual …

Active contour models (ACMs) have been widely used in image segmentation to segment
objects. However, when it comes to segmenting images with severe intensity …