Lung automatic seeding and segmentation: A robust method based on relaxed oriented image foresting transform

In radiotherapy, medical image segmentation is performed to achieve a more structured view of the patient’s anatomical region. Automatic segmentation methods aim to eliminate the significant time investment required by the manual and semi-automatic segmentation processes that are most commonly used today. The present work describes an innovative seed-based automatic segmentation method for computed tomography (CT) images, known as LUNg Automatic Seeding and Segmentation (LUNAS). The study compares LUNAS with other segmentation algorithms from the Lung CT Segmentation Challenge (LCTSC), which are based on neural networks and multi-atlas approaches. The findings indicate that LUNAS achieved a Dice accuracy metric of 0.96 and 0.97 for the left and right lungs, respectively, matched the top-performing DL methods in the competition. Additionally, other state-of-the-art methods were evaluated for comparison, including one seed-based method similar to LUNAS, as well as a deep learning method. Using three other public thoracic CT image datasets, a detailed and fair analysis was performed to compare the algorithms indicating the effectiveness of LUNAS. LUNAS also has the ability to segment other regions, such as the trachea, bones, and skin. • LUNAS achieves state-of-the-art accuracy in lung CT segmentation, outper-forming traditional methods and matching deep learning approaches. • The method employs a novel automatic seed generation strategy combined with the Relaxed Oriented Image Foresting Transform (ROIFT). • LUNAS provides robust segmentation without requiring GPU acceleration, making it computationally efficient. • The method demonstrates superior adaptability, performing well on multiple publicly available thoracic CT datasets. • The methodology is adaptable to other anatomical structures such as trachea, bones, and skin.