Blood Segmentation

Blood segmentation, the automated identification and delineation of blood within medical images, aims to improve diagnostic accuracy and efficiency across various applications. Current research heavily utilizes deep learning, employing architectures like U-Net and Swin Transformers, to achieve high-accuracy segmentation in diverse contexts such as blood cell analysis, surgical hemorrhage monitoring, and post-hemorrhage brain scans. These advancements offer significant potential for faster and more objective diagnoses, enabling improved patient care and facilitating the development of automated surgical tools.