Lung Nodule Segmentation

Lung nodule segmentation aims to automatically identify and delineate lung nodules in medical images (like CT scans and X-rays) to aid in early lung cancer detection. Current research heavily utilizes deep learning, particularly encoder-decoder networks and variations incorporating attention mechanisms and normalizing flows, to improve segmentation accuracy and address challenges posed by nodule heterogeneity and size variability. These advancements are crucial for improving diagnostic accuracy, potentially leading to earlier and more effective lung cancer treatment, and facilitating more informed clinical decision-making by providing quantitative measures of uncertainty in segmentation results.