Metal Segmentation

Metal segmentation in medical imaging, particularly X-ray and cone-beam computed tomography (CBCT), aims to accurately identify and delineate metal implants within images to improve diagnostic quality and treatment planning. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs) and vision transformers (ViTs) for this task, often leveraging simulated data to overcome limitations in the availability of labeled clinical datasets. Accurate metal segmentation is crucial for metal artifact reduction (MAR) algorithms, leading to improved image quality and more reliable analysis of anatomical structures near metallic implants, ultimately benefiting patient care.