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Lung automatic seeding and segmentation: A robust method based on relaxed oriented image foresting transform
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DOI：10.1016/j.bspc.2026.109537.png)
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Abstract
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• 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.
Keywords:
Automatic segmentation
Computed tomography
Medical image analysis
Relaxed oriented image foresting transform
Seed-based segmentation
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