.png){kind=icon}
Return
Lung automatic seeding and segmentation: A robust method based on relaxed oriented image foresting transform
.png){kind=icon}
OA
AI
DOI：10.1016/j.bspc.2026.109537.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• 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
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.9
Papers:
1.0W
Citations:
2.4W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSave
ShareSave
View more