  Accurate automated extraction of brain vessel centerlines from CTA images
plays an important role in diagnosis and therapy of cerebrovascular diseases,
such as stroke. However, this task remains challenging due to the complex
cerebrovascular structure, the varying imaging quality, and vessel pathology
effects. In this paper, we consider automatic lumen segmentation generation
without additional annotation effort by physicians and more effective use of
the generated lumen segmentation for improved centerline extraction
performance. We propose an automated framework for brain vessel centerline
extraction from CTA images. The framework consists of four major components:
(1) pre-processing approaches that register CTA images with a CT atlas and
divide these images into input patches, (2) lumen segmentation generation from
annotated vessel centerlines using graph cuts and robust kernel regression, (3)
a dual-branch topology-aware UNet (DTUNet) that can effectively utilize the
annotated vessel centerlines and the generated lumen segmentation through a
topology-aware loss (TAL) and its dual-branch design, and (4) post-processing
approaches that skeletonize the predicted lumen segmentation. Extensive
experiments on a multi-center dataset demonstrate that the proposed framework
outperforms state-of-the-art methods in terms of average symmetric centerline
distance (ASCD) and overlap (OV). Subgroup analyses further suggest that the
proposed framework holds promise in clinical applications for stroke treatment.
Code is publicly available at https://github.com/Liusj-gh/DTUNet.
