Ultrasound Guided

Ultrasound-guided procedures are increasingly leveraging advanced image processing and machine learning to improve accuracy and efficiency in various surgical contexts, including liver resection, brain tumor removal, and spinal surgery. Current research focuses on developing deep learning models, such as convolutional neural networks and graph convolutional networks, for tasks like real-time anatomical landmark detection, inter-modal registration error estimation, and bone surface segmentation, often incorporating techniques like contrastive learning and focal modulation to enhance performance. These advancements aim to improve the precision and safety of minimally invasive surgeries by providing surgeons with more accurate and reliable real-time imaging guidance, ultimately leading to better patient outcomes.