Lung Originated Tumor Segmentation

Lung-originated tumor segmentation from computed tomography (CT) scans aims to automatically identify and delineate lung tumors in medical images, improving diagnostic speed and accuracy. Current research focuses on leveraging deep learning models, including variations of UNet and transformer architectures, often incorporating techniques like self-supervised learning, teacher-student training, and data augmentation to address challenges such as limited annotated data and variability in image quality. Accurate and efficient automated segmentation holds significant potential for improving lung cancer diagnosis and treatment planning, ultimately impacting patient outcomes and streamlining clinical workflows.