Tubular Structure

Research on tubular structure segmentation focuses on accurately identifying and delineating tube-like objects in images, crucial for applications ranging from medical imaging to remote sensing. Current efforts leverage deep learning architectures, such as U-Net variations and novel convolutional approaches, often incorporating fractal features or skeleton-based losses to improve segmentation accuracy and topological consistency, particularly for thin or complex structures. These advancements aim to enhance efficiency and robustness, addressing challenges like computational cost and preserving connectivity in segmented structures, ultimately improving the reliability of downstream analyses in various fields.