Ultrasound Image Segmentation

Ultrasound image segmentation aims to automatically identify and delineate anatomical structures and lesions within ultrasound images, facilitating faster and more accurate diagnoses. Current research heavily utilizes convolutional neural networks (CNNs), often incorporating architectures like U-Net and its variants, along with transformer-based models and the Segment Anything Model (SAM), adapted and enhanced for the unique challenges of ultrasound imagery (e.g., low contrast, noise). These advancements hold significant promise for improving diagnostic accuracy, reducing workload for medical professionals, and enabling new applications in areas such as robotic-assisted procedures and quantitative image analysis.