Hemorrhage Area

Hemorrhage area analysis focuses on accurately identifying and quantifying bleeding in medical images, primarily to improve diagnosis and treatment of conditions like hemorrhagic stroke. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), often combined with other architectures like U-Nets or graph neural networks (GNNs), to segment hemorrhage regions from various imaging modalities such as CT scans and fundus photography. These advancements aim to automate and improve the speed and accuracy of hemorrhage detection, leading to faster diagnosis and potentially better patient outcomes, particularly in time-critical situations. The development of non-invasive methods, such as ultra-wideband microwave imaging, also shows promise for expanding diagnostic capabilities.