When doctors review brain scans, each detail of the picture—each pixel or voxel—must be painstakingly labeled. The cerebral cortex, hippocampus, ventricles, and other structures all need to be marked ...

Researchers have successfully developed the technology that can accurately segment different body organs by effectively learning medical image data used for different purposes in different hospitals, ...

A new artificial intelligence (AI) tool could make it much easier-and cheaper-for doctors and researchers to train medical imaging software, even when only a small number of patient scans are ...

AI algorithms analyse complex medical images with speed and precision, supporting early disease detection.Radiology and ...

Annotating regions of interest in medical images, a process known as segmentation, is often one of the first steps clinical researchers take when running a new study involving biomedical images. For ...

Accurate segmentation of medical images is essential for clinical decision-making, and deep learning techniques have shown remarkable results in this area. However, existing segmentation models that ...

Tyche is a machine-learning framework that can generate plausible answers when asked to identify potential disease in medical images. By capturing the ambiguity in images, the technique could prevent ...

Medical image segmentation is a fundamental component of many clinical applications such as computer-aided diagnosis, radiotherapy planning, and preoperative planning. Its accuracy and stability ...